US20060010126A1 - Systems and methods for interactive search query refinement - Google Patents
Systems and methods for interactive search query refinement Download PDFInfo
- Publication number
- US20060010126A1 US20060010126A1 US11/192,724 US19272405A US2006010126A1 US 20060010126 A1 US20060010126 A1 US 20060010126A1 US 19272405 A US19272405 A US 19272405A US 2006010126 A1 US2006010126 A1 US 2006010126A1
- Authority
- US
- United States
- Prior art keywords
- candidate
- term
- terms
- document
- ranked
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3325—Reformulation based on results of preceding query
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
- Y10S707/99935—Query augmenting and refining, e.g. inexact access
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99943—Generating database or data structure, e.g. via user interface
Abstract
Description
- This application claims priority to U.S. Patent Application Ser. No. 60/456,905 entitled “Systems and Methods For Interactive Search Query Refinement” filed Mar. 21, 2003, attorney docket number 10130-044-888, which is hereby incorporated by reference in its entirety.
- The present invention relates to the field of search engines, such as search engines for locating documents in a database or documents stored on servers coupled to the Internet or in an intranet, and in particular the present invention relates to systems and methods for assisting search engine users in refining their search queries so as to locate documents of interest to the users.
- Developing search expressions that both convey a user's information need and match the way that need is expressed within the vocabulary of target documents has long been recognized as a difficult cognitive task for users of text search engines. A large majority of search engine users begin their search for a document with a query having only one or two words, and are then disappointed when they do not find the document or documents they want within the first ten or so results produced by the search engine. While user satisfaction can be improved, at least for some searches, by improving the manner in which results are ranked, very broad search queries cannot satisfy the more specific information desires of many different search engine users. One way to help a user refine a query expression is to offer term suggestions, just as a librarian might do so in a face-to-face interaction with an information seeker. Doing this automatically, however, is quite different, since the system must “guess” which terms, out of hundreds that may be conceptually related to a query, as most likely to be relevant to users conducting a search. Common approaches for choosing related terms include consulting an online thesaurus or a database of prior logged queries (that can be searched to find previous queries that contain one or more words in the current query). A weakness of such approaches is that there is no guarantee that the related terms so generated actually reflect the subject matter or vocabulary used within the corpus of documents itself. For this reason, alternative approaches that attempt to glean related terms dynamically from the actual results of a query have received much interest.
- Some prior approaches that use a search result set to generate refinement suggestions include term relevance feedback (e.g. Vélez et al., Fast and Effective Query Refinement, in Proceedings of SIGIR'97, pp. 6-15), Hyperindex (Bruza and Dennis, Query Reformulation on the Internet: Empirical Data and the Hyperindex Search Engine, in Proceedings of RIAO'97, pp. 500-509), Paraphrase (Anick and Tipimeni, The Paraphrase Search Assistant: Terminological Feedback for Iterative Information Seeking, in Proceedings of SIGIR'99, pp. 153-159) and clustering (Zamir and Etzioni, Web Document Clustering: A Feasibility Demonstration, in Proceedings of SIGIR'98, pp. 46-54). Most relevance feedback methods have been designed for partial match search engines and typically involve broadening a query expression by the addition of multiple weighted terms derived from computations over a subset of retrieved documents explicitly tagged as relevant or non-relevant by a user. Hyperindex runs a syntactic analyzer over snippets returned by a search engine to extract noun phrases that contain the query term. Paraphrase extracts noun phrases from result set documents and chooses feedback terms to display based on lexical dispersion. Clustering approaches attempt to cluster result set snippets and derive representative query terms from the terms appearing within the respective clusters. While many of these approaches are functional, they are somewhat unsatisfactory for very large web search engines, either for reasons of runtime performance or relevance of feedback terms generated. There remains a need in the art for effective methods for assisting a user in identifying relevant search terms to improve a search.
- To better understand the limitations of the prior art, a closer review of Vélez et al., Fast and Effective Query Refinement, in Proceedings of SIGIR'97, pp. 6-15, is warranted. Vélez et al. provides a system and method for query refinement in which terms from automated suggestions are added to an initial query in order to refine the initial query. In Vélez et al., the authors build upon the generic query refinement program DM. As put forth in Vélez et al., DM has the following steps:
-
- Let
- C=document corpus
- q=user query
- r=number of matching documents to consider
- Wfcn(S)=algorithm specific weight term set S
- Then,
- 1. Compute the set of documents D(q)εC that match the query q.
- 2. Select a subset Dr(q) of top r matching documents
- 3. Compute the set of terms T(q) from the documents Dr(q) such that T(q)={t|∃d εDr(q): tεd} where d is a document and t is a term.
- 4. Compute the subset S of n terms from T(q) with the highest weight Wfcn(S).
- 5. Present S to the user as the set of term suggestions.
As noted in Vélez et al., this approach is unsatisfactory because it is an expensive run time technique. In other words, it will take an unsatisfactory amount of time to compute the set of term suggestions S using DM in cases where the document database (corpus) is large.
- Let
- Vélez et al. seeks to improve on the speed of DM by precomputing a substantial amount of the work that is done dynamically by DM. In this precomputation phase, Vélez et al. generates a data structure that maps each single-word term t in the corpus to a respective set of terms m that the DM algorithm would suggest given the single term query t. Then, at run-time, an arbitrary query is received from the user. The query typically comprises a set of terms. In response to the query, Vélez et al. collects the respective sets of terms m corresponding to each of the terms in the query and merges each of these sets into a single set that is then returned to the user as suggestions for an improved search. For example, consider the case in which the user enters the query “space shuttle”. In this instance Vélez et al. could obtain the set of terms m that have been precomputed for the word “space” and the set of terms m that have been precomputed for the word “shuttle” and will merge them together in order to derive a set of suggested terms for the query “space shuttle”.
- While this approach improves runtime performance by precomputing a subset of term relationships off-line, the Vélez et al. approach has drawbacks. First, there is a context problem. The Vélez et al. approach relies on the assumption that the set of terms m relevant to a given term t is the same regardless of whether the term t appears by itself or as part of a multi-term query. However, this is assumption is not always true. A term appearing within a multi-term phrase can in some instances express a completely different meaning relative to the term appearing by itself. Because of the underlying assumption in Vélez et al., the approach can potentially lead to inappropriate search term suggestions in some instances or else miss other suggestions that would be more relevant within the context of the entire query. Second, when the corpus (document database) changes, the Vélez et al. approach requires that sets of terms m respectively associated with terms t in the corpus be recomputed because each set of terms m depends on the contents of a plurality of files in the corpus including, possibly, files that have recently been added to the corpus.
- Xu and Croft, SIGIR'97, pp. 4-11 describe another approach in which sets of terms that are related to a given concept are precomputed before a search query, which may include several concepts (search terms), is received. Like the Vélez et al. approach, the Xu and Croft methods relies on the construction of static cross document data structures and statistics that necessitate extensive recomputation of terms associated with concepts as the corpus changes over time. Accordingly, the computational demands of Xu and Croft are unsatisfactory for very large, dynamic document databases.
- Given the above background, it would be desirable to provide assistance to users in refining their search queries into more narrowly defined queries, so as to produce search results more to their liking.
- The present invention provides an improved method for refining a search query that is designed to retrieve documents from a document index. The present invention is advantageous because it does not rely on cross document data structures or global statistics that must be recomputed each time the corpus is updated. Further, the present invention requires significantly less I/O resources at query time (run time) because fewer results need to be fetched at run time than in known methods to produce a short list of relevant suggestions that includes a mix of phrases, single word terms, and specializations (phrases including a query term). In the present invention, each document in the document index is processed at some time prior to the query, for example during the generation of the document index. In this processing, each document in the document index is examined to determine if the document includes any terms suitable for inclusion in a set of ranked candidate terms for the document. When the document includes such terms, the document index entry for the document is configured to include a set of terms associated with the document. This set of terms is called a set of ranked candidate terms.
- When a query is received, an initial group of documents are retrieved from the document index. The initial group of documents is ranked by relevance to the query. The “initial group” of documents can be a subset of the full set of documents identified as being potentially relevant to the query. In one embodiment, the number of documents in the initial group is the lesser of all the documents identified as being potentially relevant to the query and a parameter value, typically between 20 and 200 (e.g., 50). Next, a weighting function is applied to each candidate term that appears in any set of ranked candidate terms that is associated with a document in the initial group of ranked documents. Top scoring candidate terms are presented, in response to the query, along with the initial group of ranked documents. User selection of one of the presented candidate terms results in the addition of the term to the original search query.
- One aspect of the invention provides a method of refining a received query. The received query is processed so as to generate an initial group of ranked documents corresponding to the received query. Each document in all or a portion of the documents in the initial group of ranked documents is associated with a respective set of ranked candidate terms. Each candidate term in the various respective sets of candidate terms is embedded within a document in the initial group of ranked documents. Each candidate term can be a word or a phrase. Furthermore, in a preferred embodiment, the various respective sets of candidate terms are constructed at a time prior to processing the received query. The method continues with the selection of a subset of candidate terms that are in one or more of the various respective sets of ranked candidate terms. A selection function is used to select this subset of candidate terms. Then, in response to the received query, the initial group of ranked documents and the subset of candidate terms are presented. In some embodiments, the processing, selecting, and presenting is repeated using a revised query that includes the original received query and a candidate term from the subset of candidate terms.
- In some embodiments, a set of candidate terms associated with a document is constructed by comparing a term in the document to a master list of candidate terms. When the term is in the master list of candidate terms, the term is added to the set of candidate terms associated with the document as a candidate term. In some embodiments, the master list of candidate terms includes more than 10,000,000 candidate terms. This comparing is repeated until a maximum number of terms in the document has been considered or a threshold number of unique terms has been considered. Then a weighting and/or selection function is applied to the set of candidate terms to produce a set of ranked candidate terms. Typically, this weighting and/or selection function ranks the candidate terms and then applies a cutoff in which only high ranked terms are retained. In some embodiments, the master list of candidate terms is optimized for a specific language (e.g., English, Spanish, French, German, Portuguese, Italian, Russian, Chinese, or Japanese). In some embodiments, each document in all or a portion of the documents in the initial group of ranked documents is in the same language for which the master list of candidate terms has been optimized.
- In some embodiments, each document in a document index is classified at a time prior to the query process (e.g., during initial document indexing). In some embodiments, there are two possible classes, a first family friendly class and a second non-family friendly class. A designation of the classification of the document is included in the document index.
- In some embodiments a single-word candidate term in a set of ranked candidate terms that is in fact a subset (substring) of a more complex term in the set of ranked candidate terms is discarded. Further, the more complex term is given credit for the number of instances the simpler term appeared in all or the upper portion of the document associated with the set of ranked candidate terms. This discarding and crediting is repeated until there is no single-word candidate term that is a subset of a more complex candidate term in the set of ranked candidate terms. Furthermore the same procedure may be applied to multi-word candidate terms that are subsets of more complex terms.
- In some embodiments a candidate term in a set of ranked candidate terms that is an orthographic or inflectional variant of a second term in the set of ranked candidate terms is discarded. Further, the second term is given credit for the number of instances the orthographic or inflectional variant term appeared in all or the upper portion of the document associated with the set of ranked candidate terms. This discarding and crediting is repeated until there is no term that is an orthographic or inflectional variant of another term in the set of ranked candidate terms. In some instances, the second term is rewritten in the candidate set as a combined term that includes both (e.g., multiple) orthographic or inflectional variants, with the variant that appeared most in all or an upper portion of the associated document appearing first in the combined term. In some embodiments, when the combined term is selected for inclusion in the subset of candidate terms presented, only the first portion of the combined term is presented to the user.
- Some embodiments of the present invention provide various selection functions that are used to select the subset of candidate terms to be presented in response to a query. In some embodiments, this selection function takes advantage of the information that is found in the sets of candidate terms associated with top-ranked documents in the initial group of ranked documents. This information includes two forms of ranking. First, the documents are ranked. Second, each candidate term in each set of ranked candidate terms associated with a document in the initial group of ranked documents is ranked.
- In one embodiment, the selection function comprises: (i) applying a weighting function to each candidate term in each respective set of ranked candidate terms that is associated with a top-ranked document in the initial group of ranked documents. As used herein, each top-ranked document in the initial group of ranked documents is a document that has a rank that is numerically less than some threshold ranking (e.g. 50, that is, the top-ranked document is in the top 50 documents in the initial group of ranked documents returned for the query). For example, consider the case in which the initial group of ranked documents includes 100 documents and the threshold ranking is fifty. Then, the first fifty documents will be considered top-ranked documents. Those candidate terms receiving a highest weight are included in the subset of candidate terms that are presented along with the query results. In some embodiments, the weight that is applied to a candidate term by the weighting function is determined in accordance with a number of sets of candidate terms associated with top-ranked documents that the candidate term appears in, the average position of the candidate term in each such set of ranked candidate terms, by whether a term in the received query is in the candidate term, by a number of characters in the candidate term, or by the average rank position of the top-ranked documents that include the term in an associated set of candidate terms. In some embodiments, the weight that is applied to a candidate term by the weighting function is determined in accordance with any combination or any weighted subset of TermCount, TermPosition, ResultPosition, TermLength, and QueryInclusion, where
-
- TermCount is the number of sets of ranked candidate terms that both (i) include the candidate term and (ii) are respectively associated with a top-ranked document,
- TermPosition is a function (e.g., an average) of the position of the candidate term in those sets of ranked candidate terms that both (i) include the candidate term and (ii) are respectively associated with a top-ranked document,
- ResultPosition is a function (e.g., an average) of the rank of those top-ranked documents that are associated with a set of ranked candidate terms that includes the candidate term,
- TermLength is a number of characters in the candidate term (candidate term complexity), and
- QueryInclusion is a value that indicates whether a term in the received query is in the candidate term.
In some embodiments, the weight that is applied to a candidate term by the weighting function is determined in accordance with the formula:
TermCount+TermPosition+ResultPosition+TermLength+QueryInclusion.
- In some embodiments, TermCount, TermPosition, ResultPosition, TermLength, and QueryInclusion are each independently weighted. In some embodiments, the weight that is applied to a candidate term by the weighting function is determined in accordance with the formula:
(TermCount*w1)+(TermPosition*(w2+(RefinementDepth*w2′)))+(ResultPosition*w3)+(TermLength*(w4+(RefinementDepth*w4′)))+(QueryInclusion*(w5+(RefinementDepth*w5′)))
where w1, w2, w3, w4, w5, w2′, w4′, and w5′ are independent weights and RefinementDepth is a number of times said processing has been performed for said received query. - In some embodiments, the selection function comprises determining, for each document in the initial group of ranked documents, the classification of the document. Then, when a threshold percentage of the set of documents belong to a first classification (e.g., a family friendly category), all sets of ranked candidate terms that belong to documents that are members of a second classification (e.g., a non family friendly category) are not used to form the subset of candidate terms.
- Another aspect of the invention provides a computer program product for use in conjunction with a computer system. The computer program product comprises a computer readable storage medium and a computer program mechanism embedded therein. The computer program mechanism comprises a query refinement suggestion engine for refining a received query. The engine comprises instructions for processing the received query so as to generate an initial group of ranked documents corresponding to the received query. Each document in all or a portion of the documents in the initial group of ranked documents is associated with a respective set of ranked candidate terms such that each candidate term in the respective set of ranked candidate terms is embedded within the document. Each respective set of ranked candidate terms is identified at a time prior to the processing of the received query. The engine further comprises instructions for selecting, in accordance with a selection function, a subset of candidate terms that are in one or more of the respective sets of candidate terms. Further, the engine comprises instructions for presenting, in response to the received query, the initial group of ranked documents and the subset of candidate terms.
- Still another aspect of the present invention provides a document index data structure comprising a plurality of uniform resource locators (URLs). Each URL designates a respective document. Each document in all or a portion of the respective documents designated by the plurality of URLs is associated with a respective set of ranked candidate terms. Each candidate term in a respective set of ranked candidate terms comprises candidate terms that are embedded in the document associated with the set of ranked candidate terms. Furthermore, these candidate terms are ranked by a weighting function. In some embodiments, a respective set of ranked candidate terms is created by
-
- (A) comparing a term in the document associated with the respective set of ranked candidate terms to a master list of candidate terms, wherein, when the term is in the master list of candidate terms, the term is added to the respective set of ranked candidate terms as a candidate term;
- (B) repeating the comparing until a maximum number of terms in the document has been considered;
- (C) ranking the candidate terms in accordance with a weighting function thereby forming the ranked candidate terms.
- The aforementioned features and advantages of the invention as well as additional features and advantages thereof will be more clearly understood hereinafter as a result of a detailed description of preferred embodiments of the invention when taken in conjunction with the drawings.
-
FIG. 1 illustrates a client computer submitting a query to a search engine. -
FIG. 2 illustrates a search results page, including query refinement suggestions, produced in accordance with an embodiment of the present invention. -
FIG. 3 is a block diagram of a search engine server. -
FIG. 4 is a block diagram of a search engine index. -
FIG. 5 is a flow chart of a document indexing method. -
FIG. 6 is a flow chart of a procedure for processing a query submitted by a user. - Like reference numerals refer to corresponding parts throughout the several views of the drawings.
- In a typical embodiment, the present invention generates, in an efficient manner, a small set (10-20) of query refinement suggestions (subset of candidate terms) that are potentially highly relevant to a user's query and reflect the vocabulary of target documents.
- As shown in
FIG. 1 , a search query is submitted by aclient computer 100 to asearch engine server 110. Upon receiving the search query,search engine server 110 identifies documents indocument index 120 that are relevant to the search query. Further,search engine server 110 ranks the relevant documents by, for example, their relevance to the search query among other ranking factors. A description of this group of ranked documents (search results) is then returned toclient computer 100 as a group of ranked documents. In the present invention, additional information, in the form of a subset of candidate terms (search refinement suggestions), is returned to the client computer along with the initial group of ranked documents. - Before turning to details on how
server 110 generates the subset of candidate terms, a screen shot of search results and search refinement suggestions returned by an embodiment ofsearch engine server 110 is provided asFIG. 2 so that the advantages of the present invention can be better understood. InFIG. 2 , a user provides an initial query (a received query) 132. Whenfind button 134 is pressed,query 132 is sent fromclient computer 100 tosearch engine server 110. Upon receivingquery 132,search engine server 110 processes receivedquery 132 and sends search results and search refinement suggestions back toclient computer 100 in the form of an initial group of ranked documents and a subset of candidate terms. The subset of candidate terms is displayed inpanel 140 ofinterface 180. Specifically eachterm 136 in the subset of candidate terms is displayed inregion 140 along with atag 138. Concurrently, a listing of search results (top-ranked documents in an initial list of ranked documents) is displayed inpanel 142. The systems and methods of the present invention are directed towards identifyingterms 136 that can narrow, change or improve theoriginal query 132. When the user presses atag 138, theterm 136 that corresponds to thetag 138 is added toinitial query 132 and the whole process repeats with the new query. When the user presses anothertag 139, theterm 136 that corresponds to thetag 138 replaces theinitial query 132 and the search engine server processes thatterm 136 as a new query. In embodiments not shown, one or more additional tags corresponding to eachterm 136 can be added to thepanel 140. In one example, there is a tag that is used to add thecorresponding term 136 to an exception list. To illustrate, when the original query is “A” and the user presses the exclusion tag for the term “B”, the new query becomes “A” and not “B”. In addition to the subset of terms displayed inpanel 140, the initial group of ranked documents is displayed inpanel 140. To save bandwidth betweencomputer 100 andserver 110, in typical embodiments, the initial group of ranked documents typically only includes an indicia of each document in the initial group of ranked documents so that the user can determine the nature of each of the documents in the initial ranked documents. Such indicia is still referred to herein as an initial group of ranked documents. - An overview of the systems and methods of the present invention has been disclosed. From this overview, the many advantages and features of the present invention are apparent. The novel algorithms of the present invention automatically provide a user with a list of suggested
terms 136 that can be used to improve an initial query. For example, inFIG. 2 , theinitial query 132 is “space shuttle”. In response to this initial query, an embodiment of the present invention provides a subset of candidate terms that includesterms 136 such as “Challenger Disaster”. Addition of the term “Challenger Disaster” to the initial query or replacement of the initial query with the term “Challenger Disaster” provides the user with a query that quite possibly more closely matches the interests of the user. By using the novel subset of candidate terms, a user can build an improved query without analyzing documents (or indicia thereof) within the initial group of ranked documents. Thus, using the present invention, there is no longer a need to determine why an initial query produced too many (or too few) results or results that are not directly related to the informational needs of the user. - Now that an overview of the invention and advantages of the present invention have been presented, a more detailed description of the systems and methods of the present invention will be disclosed. To this end,
FIG. 3 illustrates asearch engine server 110 in accordance with one embodiment of the present invention. In a preferred embodiment, thesearch engine server 110 is implemented using one ormore computer systems 300, as schematically shown inFIG. 3 . It will be appreciated by those of skill in the art, that search engines designed to process large volumes of queries may use more complicated computer architectures than the one shown inFIG. 3 . For instance, front end set of servers may be used to receive and distribute queries among a set of back end servers that actually process the queries. In such a system, thesystem 300 shown inFIG. 3 would be one of the back end servers. - The
computer system 300, will typically have a user interface 304 (including adisplay 306 and a keyboard 308), one or more processing units (CPU's) 302, a network orother communications interface 310,memory 314, and one or more communication busses 312 for interconnecting these components.Memory 314 can include high speed random access memory and can also include non-volatile memory, such as one or more magnetic disk storage devices (not shown).Memory 314 can include mass storage that is remotely located from the central processing unit(s) 302. Thememory 314 preferably stores: -
- an
operating system 316 that includes procedures for handling various basic system services and for performing hardware dependent tasks; - a
network communication module 318 that is used for connecting thesystem 300 to various client computers 100 (FIG. 1 ) and possibly to other servers or computers via one or more communication networks, such as, the Internet, other wide area networks, local area networks (e.g., a local wireless network can connect the client computers 1100 to computer 300), metropolitan area networks, and so on; - a
query handler 320 for receiving a query from aclient computer 100; - a
search engine 322 for searching adocument index 352 for documents related to a query and for forming an initial group of ranked documents that are related to the query, and - a query
refinement suggestion engine 324, for implementing many aspects of the present invention.
- an
- Query
refinement suggestion engine 324 can include executable procedures, sub-modules, tables and other data structures. In one embodiment,refinement suggestion engine 324 includes: -
- a
selection function 326 for identifying a subset of candidate terms for presentation in conjunction with an initial group of ranked documents; and - a
results formatting module 328 for formatting the subset of candidate terms and the initial group of ranked documents for presentation.
- a
- The methods of the present invention begin before a
query 132 is received byquery handler 320 with the actions ofdocument indexer 344.Document indexer 344 builds adocument index 352 using web crawling and indexing technology. However, in addition to this conventional functionality,document indexer 344 includes novel program modules that further process documents indocument index 352. For instance,document indexer 344 includes a “set of candidate terms constructor” 346. In a preferred embodiment,constructor 346 examines each document indocument index 352. In other embodiments, only documents meeting predefined criteria (e.g., documents containing text in one of a predefined set of languages) are examined by theconstructor 346. - For each document examined,
constructor 346 determines whether the document includes any candidate terms embedded in the document. There are many different ways in which constructor 346 can accomplish this task and all such methods are included within the scope of the present invention. In one embodiment, the task is accomplished by matching terms from the document with master list of candidate terms 342. Master list ofcandidate terms 342 includes all possible candidate terms. In some embodiments list 342 is a Unix style text file with a list of valid candidate terms. A representative format forlist 342 is one candidate term per line, each candidate term inlist 342 unique, UTF-8 encoded, with all commas, tabs, line ends, and @ symbols stripped. In some embodiments, the master list is restricted to nouns and noun phrases (the kinds of terms most likely to be useful as query terms), with any noun phrases of limited query refinement value explicitly removed. - In typical embodiments only a first portion of each document in
document index 352 is examined for candidate terms. For example, in some instances only the first 100,000 bytes of each document indocument index 352 is examined byconstructor 346. In some embodiments,constructor 346 examines a document indocument index 352 until a maximum number of terms (e.g., 100, 1000, 5000, etc.) in the document have been considered. In some embodiments, the search for candidate terms in a document is terminated when a threshold number of unique terms in the document have been found to occur within master list 342 (e.g., 1000 terms). - Some embodiments of the present invention provide more than one master list of candidate terms 342. Each
master list 342 is optimized for a different language. For example, afirst list 342 is optimized for English and asecond list 342 is optimized for Spanish. Thus, theEnglish list 342 will include informative terms that are found in English documents whereas theSpanish list 342 will include informative terms that are found in Spanish documents. Similarly, some embodiments of the present invention include lists that are optimized for French, German, Portuguese, Italian, Russian, Chinese, or Japanese. In some embodiments of the present invention, lists 342 are optimized for other types of categories. For example, in some embodiments, alist 342 is optimized to include scientific terms, fashion terms, engineering terms, or travel terms. However, in a preferred embodiment, eachmaster list 342 is as inclusive as possible of informative terms. In fact, amaster list 342 can include more than 10,000,000 terms, and typically includes significantly more than 1,000,000 terms. Each of these terms can be a word or a phrase. For clarity, a representative phrase is “Challenger Disaster.” - Methods for determining the predominant language used in a document are well known in the art. Thus, in some embodiments of the present invention,
constructor 346 uses such methods to (i) determine the language of the document being examined, and then (ii) use themaster list 342 that is optimized for the same language as the document. - In the case where one or more candidate terms that are in
master list 342 are embedded in an upper portion (e.g., in the first 100 kilobytes) of a document inindex 352, the net result of the examination of the document by constructor 346 is the identification of such terms. When such terms are identified byconstructor 346, they are added to a data structure associated with the document in a ranked form. This data structure is referred to as a set of candidate terms. Afterindex 352 has been examined byconstructor 346, each document inindex 352 that has embedded candidate terms in their upper portions will be associated with a respective set of candidate terms that includes such terms. Thus, for example, if there are two documents, A and B, inindex 352 that include candidate terms, a first set of candidate terms will be associated with document A and a second set of candidate terms will be associated with document B. The first set of candidate terms will include each candidate term that is embedded in an upper portion of document A and the second set of ranked candidate terms will include each term that is embedded in an upper portion of document B. In practice, each set of candidate terms is internally ranked to form a respective ranked set of candidate terms as disclosed in further detail below. -
FIG. 4 illustrates how examination ofdocuments 402 in adocument index 352 by constructor 346 results in a modification ofdocument index 352. Before constructor 346 examined documents inindex 352, eachdocument 402 inindex 352 includes the uniform resource location (URL) 406 of thedocument 402 as well as a set of feature values 408. The feature values 408 include metadata associated with the document, and include values that assist the search engine when ranking documents identified as being potentially relevant to a query. The feature values can include an indication of the file format of the document, the length of the document, the number of known inbound links (from other documents) to the document, the title of the document (e.g., for displaying when the document is selected as being responsive to a query), and so on. After adocument 402 has been examined by the constructor 346 (FIG. 3 ), a set ofcandidate terms 410 is associated with thedocument 402. - In some embodiments of the present invention, the method by which a term in a document is matched with a candidate term in
list 342 is done in such a manner that ensures that the term is matched with the most complex candidate term possible inlist 342. To illustrate, consider the case where the term “A B” is embedded in a document inindex 352, where A and B are each words. Further, assume thatlist 342 includes “A”, “B”, and “A B”. When this arises, the term “A B” in the document will be matched with “A B” inlist 342 and not “A” or “B”. There are a number of ways in which such matching can be accomplished and all such matching schemes are within the scope of the present invention. One such matching approach uses a “greedy left to right algorithm” having the following logic: - for each sentence of the form ‘A B C D . . . ’ in the document examined:
- is A a prefix for a candidate term in
list 342?- Yes: Is ‘A B’ a prefix for a candidate term in
list 342?- Yes: Is ‘A B C’ a prefix for a candidate term in
list 342?- Yes ->continue drilling through the sentence in like manner
- No: add ‘A B’ to the set of
candidate terms 410 associated with the document and move to C and consider ‘C D E F . . . ’
- a No: add ‘A’ to the set of
candidate terms 410 associated with the document and move to B and consider ‘B C D E . . . ’
- Yes: Is ‘A B C’ a prefix for a candidate term in
- No: move to B and begin consider ‘B C D E . . . ’
An algorithm such as this, where a “sentence” is some arbitrary amount of the document, such as a line, or the portion of document between two phrase boundaries or other break points, and ‘A B C D . . . ’ are each words in a term, ensures that the most complex term inlist 342 is matched to a term in the document. In related approaches,constructor 346 discards a first candidate term in the set ofcandidate terms 410 when the first candidate term is a subset of a second candidate term in the set of candidate terms.
- Yes: Is ‘A B’ a prefix for a candidate term in
- is A a prefix for a candidate term in
- In some embodiments of the present invention, the number of times each candidate term in a set of ranked
terms 410 appears in all or an upper portion (e.g., the first 100 kilobytes) of the document associated with theset 410 is tracked. For example, if the candidate term “A” inset 410 appears 12 times in the an upper portion of the document associated withset 410, then an indication that term “A” appears twelve times in the document in noted and used in a weighting scheme designed to determine which candidate terms will remain in the final set of ranked candidate terms. - In some embodiments, the indication of the number of times a term appears in the associated document is upweighted in each instance where the term appears within a first threshold number of words of the document. For instance, consider the case where the value of the first threshold is fifteen words. Further, in this exemplary case, the candidate term “A” appears exactly twice. The first appearance of phrase “A” is before the fifteen word limit and the second appearance of “A” is after the fifteen word limit. In the weighting scheme used in this exemplary case, words appearing within the first fifteen words receive twice as much weight. Thus, in the set of
candidate terms 402 associated with the document, the candidate term “A” will be listed along with an indication that the term appears (2*1+1), or 3 times in an upper portion of the document. Those of skill in the art will appreciate that more complex forms of the first threshold are possible. For example, the weight applied to the candidate term count can be a function of the position of the candidate term in the document. For example, it could be a linear function (or a non-linear function, or a piecewise linear function) having a maximum at the beginning of the document and a minimum at the end of the document. Alternatively, the weight could be applied in baskets, where there is a large weight at the beginning of the document (first basket), a lower weight in a second portion of the document (second basket), an even lower weight in a third portion of the document (third basket), and so forth. - In embodiments where both (i) an indication of the number of times a candidate term appears in the associated document and (ii) constructor 346 discards a first candidate term in the set of ranked
candidate terms 410 when the first candidate term is a subset of a second candidate term in the set of ranked candidate terms, the second candidate term is credited with the number of time the first candidate term was identified in the document byconstructor 346. - In addition to
constructor 346,indexer 344 includesredundancy filter 348.Filter 348 is designed to remove orthographic or inflectional variants that can end up in the set of candidate terms. An orthographic variant of a term has an alternative, correct spelling for the term. An inflectional variant of a term has an alternative suffix, or accented form of the term. In some embodiments, orthographic and/or inflectional variants are stored in variants list 360 (FIG. 3 ). The job ofredundancy filter 348, then, is to ensure that no pair of candidate terms in the set ofcandidate terms 410 is invariants list 360. When a pair of candidate terms in the set ofcandidate terms 410 is invariants list 360, one term from the pair is discarded fromset 410 byfilter 348. In some embodiments, the first term in the pair will be effectively discarded fromset 410 and a second term in the pair will be preserved. However, in some embodiments, the second term will be modified such that it is combined with the discarded first term. For example, if the terms A and B are inflectional or orthographic variants, one of the terms, say A, will be discarded and the other term, B, preserved. Further the term B will be rewritten as A,B. This feature is advantageous because it preserves useful information about the underlying document that can be used by higher level modules of the present invention such as the queryrefinement suggestion engine 324. Typicallyengine 324 will only present the first (nondiscarded) term in the case where such merged orthographic or inflectional variant candidate terms appear. For example, in the case of the rewritten term A,B, only the term “A” is included in the subset of candidate terms presented inpanel 140. Typically the term that is discarded in a pair of terms appearing inlist 360 is the term that appears less often in the associated document. In some embodiments, candidate terms that only differ by the absence or presence of certain noise words (e.g., a, the, who, what, where, etc.) are folded in the same manner as candidate terms that include orthographic or inflectional variants are folded together. Likewise, in some embodiments, in instances where the only differences between two terms in a given set of candidate terms is the presence or absence of punctuation, the two terms are folded together in the same manner that candidate terms that include orthographic or inflectional variants are folded together. In some embodiments, each phrase in the set of candidate terms is converted to the same case (e.g., lowercase). An exception to this rule is that those terms that are single words of six or fewer upper case characters are not converted to lower case because it is likely that such a term is an acronym. - In embodiments where both (i) an indication of the number of times a candidate term appears in the associated document and (ii)
filter 348 discards a first candidate term in the set of candidate terms because it is an orthographic or inflectional variant of a second candidate term in the set, the second candidate term is credited with the number of time the first candidate term was identified in the document byconstructor 346. In other words, when the only difference between two candidate terms is that one of the candidate terms includes a word that is an inflectional or orthographic variant of the corresponding word in the other candidate term, one of the candidate terms is discarded. An example of this arises in the case of the candidate terms “tow truck” and “tow trucks”. In this example, the only difference between the two candidate terms is the recitation of “truck” in the first term and the recitation of “trucks” in the second term. - Many details about the
document indexer 344 have been disclosed. At this stage it is instructive to review the flow diagram ofFIG. 5 , which discloses steps taken by some embodiments ofindexer 344. After all or a portion of its other indexing duties (e.g., conventional indexing of the words in the documents found by a web crawler),indexer 344 passes control toconstructor 346, which selects a document that has been indexed (FIG. 5 , step 502). - In
step 504, a term in the document is compared to amaster list 342 of candidate terms. If the term is in the master list 342 (506—Yes), the term is added to the set ofcandidate terms 402 that is associated with the document (510). Note, thatstep 504 can involve more complex matching schemes such as the greedy left to right algorithm described above. - In some embodiments, the document to be compared is a web page. Therefore, some decisions must be made as to what constitutes a valid word suitable for comparison to
master list 342. In one approach, a document that is, in fact, a web page is parsed to find text for phrase extraction. In one embodiment, phrase matching is performed instep 504 using all ‘visible’ text plus meta page descriptions, and such phrases do not include HTML code, java-script, etc. In order to derive valid phrases, ‘phrase boundaries’ (e.g., table tags) within the web page are preserved such that an expression extracted from the document for comparison to list 342 does not span a phrase boundary. Additional examples of phrase boundaries that are used in some embodiments of the present invention include, but are not limited to punctuation like ‘.’, ‘?’, empty lines, etc. - In some embodiments of the present
invention master list 342 is a very large set of terms that are gathered from several disparate sources. Therefore, instep 504 additional filtering can be performed to make sure that only informative candidate terms are selected for inclusion in a set of candidate terms. In some embodiments, the term in the document that is compared to terms inmaster list 342 is processed prior to the comparison. For example, in some embodiments, punctuation marks are removed from a term prior to comparison tolist 342. In some embodiments, punctuation characters are replaced with a space prior to comparison tolist 342. In some embodiments a list ofnoise terms 354 is stored inmemory 314. Representative noise terms include, but are not limited to, words such as “a”, “the”, “who”, “what”, and “where.” Thus, in embodiments in which a list ofnoise terms 354 is stored inmemory 314, comparingstep 504 will first determine whether a term to be compared withmaster list 342 is in the list of noise terms 354. If it is, then the term is ignored and it is not compared withlist 342. In some embodiments, only those terms that contain at least a certain minimum threshold of characters is compared instep 504. For example, in some embodiments, only those terms that contain at least four characters are compared instep 504. - Regardless of the outcome of
decision 506, adetermination 508 is made as to whether any other terms in the document should be compared tomaster list 342 byconstructor 346. Many different conditions that can be used to determine the outcome ofdecision 508 have been disclosed (e.g., maximum number of term cutoff, maximum number of unique term cutoff, maximum number of candidate terms already inset 410, etc.). - What follows in the flow chart in
FIG. 5 are optional steps. Inoptional step 512, redundant terms are folded in the set of candidate terms associated with a document. Inoptional step 514, the document inindex 352 are classified (e.g., into a first and second classes). - There are a number of different ways in which
classification step 514 can be effected and all such ways are included within the scope of the present invention. For instance, in some embodiments, eachdocument 402 is classified into a first or second class. In a preferred embodiment the first class is a family friendly class and the second class is a nonfamily friendly class. Thedocument 402 will be classified into the second class when it includes sexually explicit, offensive, or violent language. Otherwise, it will be classified into the first class. In some embodiments, classifier module 350 (FIG. 3 ) is used to perform such a classification. Typically,classifier module 350 works by determining whether a document is intended to be sexually explicit, offensive, or to include violence. If so, the document is designated nonfamily friendly. This designation is stored in the feature values 408 (FIG. 4 ) that corresponds to the document that is associated with theclassified set 410. - At this stage there are typically a large number of candidate terms in the set of candidate terms. For example, in embodiments where as many as 1000 candidate terms can be added to a set of candidate terms, the set of candidate terms can include 1000 terms at this stage. Regardless of the number of candidate terms in each candidate term set, they have not been ranked. Thus, in
step 516 the candidate terms are ranked and then the N highest number of ranked candidate terms are allowed to remain in the candidate set and all other candidate terms are removed so as to keep only the N (e.g., 20) most representative terms in the ranked set (516). Thus, the net effect ofstep 516 is to produce a set of ranked candidate terms from the set of candidate terms. Further, instep 516, only top-ranked terms (e.g., the top 20) are allowed to remain in the set of ranked candidate terms. - Criteria or parameters used by the ranking function can include one or more of the following: the number of times each term appears in the document, whether the term appears in a predefined early portion of the document, the first position of the term in the document, and the number of characters in the term. Based on these parameters, a rank is assigned to each candidate term, and then only the N terms having the highest rank are retained in the set of ranked candidate terms. The other terms are deleted from the set. Limiting the number of candidate terms associated with each document helps keep the document index from growing excessively large and reduces the quantity of terms that need to be considered at query-time, when speed of processing is paramount. The set of ranked candidate terms for a document can be associated with a document by storing in the document's index entry (see 410,
FIG. 4 ) a set of strings (optionally compressed) or indices representing the candidate terms, where each index value points to the term in the master list of candidate terms 342. Related values can be stored in thedocument index 352 entry for a document, along with each candidate term (or a pointer to a candidate term) associated with the document, such as the term score used in the ranking process appears in the document and/or a first position of the term in the document. However, in a preferred embodiment, such additional values are not stored indocument index 352. - The process by which sets of ranked
candidate terms 410 are associated with documents indocument index 352 has been described. Attention now turns toFIG. 6 which describes a way in whichsuch sets 410 are used to construct a subset of candidate terms for presentation, in accordance with one embodiment of the invention. Instep 602, a query is received byquery handler 320. Instep 604, the query is processed thereby retrieving an initial group of ranked documents fromdocument index 352. It will be appreciated that, in some embodiments, the initial group of ranked documents can only contain indicia of the documents rather than the documents themselves. However, this indicia will include the uniform resource locator (URL) for each document in the initial set of documents. Therefore, each document can be retrieved from the Internet (or some other form of network) if subsequently requested by the user. In some embodiments, the initial set of documents is stored assearch results 340 inmemory 314 of server 300 (FIG. 3 ). Referring again toFIG. 6 , a list of suggested query refinements (subset of candidate terms) is created (606) using search results 340. - The way in which a list of suggested query refinements (subset of candidate terms) is created will depend upon whether the query is a family friendly search. In
optional step 608, a determination, for each top-ranked document (e.g., in the first fifty documents) in search results 340 (the initial group of ranked documents) is made of the classification of the document. When a threshold percentage of the top-ranked documents insearch results 340 belong to a first classification (family friendly classification), all sets 410 of candidate terms associated with a top-ranked document that do not belong to the first classification are not used in any subsequent steps inFIG. 6 . In some embodiments, classifications other than family friendly are used to classify documents during indexing (FIG. 5 ). In such embodiments, such classifications can be used instep 608 to determine which sets of ranked candidate terms will be used to construct the subset of candidate terms. In an exemplary embodiment, the classification of only the M top-ranked documents (e.g., the ten top ranked documents from the search results 340) is used to make the determination instep 608. For example, if at least eight of the ten top ranked documents is classified as being family friendly, then candidate terms from non-family friendly documents are excluded from the sets of ranked candidate terms used to create the list of suggested query refinements. - In
step 610, a subset of candidate terms that are in one or more of the respective sets of ranked candidate terms that are associated with documents insearch results 340 are selected. In one embodiment, this selection function comprises applying a weighting function to each candidate term in each respective set of rankedcandidate terms 410 that is associated with a top-ranked document in the initial group of ranked documents (search results 340). Each top-ranked document in the initial group of ranked documents has a ranking that is numerically less than a threshold ranking. In some embodiments, the top-ranked documents are the T top ranked documents, where T is a predefined number such as 50 (and is preferably in the range of 5 to 200, and is most preferably in the range of 20 to 100). Only top-ranked documents are considered instep 610 in order to maximize the chances of collecting relevant terms into the subset of candidate terms that is presented to the user. In various embodiments, only the top 5, 10, 15, 20, 50, or 100 documents are considered. Those candidate terms that receive the highest weight are included in the subset of candidate terms. In some embodiments, the number of terms in the subset of candidate terms is limited to a number less than 25. - In some embodiments, the subset of candidate terms is not built and no subset of candidate terms is presented to the user when there is fewer than a cutoff number of documents in the initial group of search results 340. For example, in one embodiment, the subset of candidate terms is not built if there are fewer than thirty-five documents in the initial group of search results 340.
- The present invention provides a number of different weighting functions for scoring the candidate terms in each of the
sets 410 associated with top-ranked documents in search results 340. These different weighting functions are used in various embodiments ofselection function 324 of engine 322 (FIG. 3 ). - In some embodiments, the weight that is applied to a candidate term by function 324 (the weighting function) is determined in accordance with the number of sets of ranked candidate terms that both (i) include the candidate term and (ii) are respectively associated with a top-ranked document. For example, consider the case in which there are fifty top-ranked documents and the candidate term “Space Shuttle” appears in the three of the sets of ranked candidate terms respectively associated with a top-ranked document. In this case, a weight of three will be applied to the candidate term “Space Shuttle”.
- In some embodiments, the weight that is applied to a candidate term by
selection function 326 is determined in accordance with a function (e.g., the average) of the candidate term in those sets of ranked candidate terms that both (i) include the candidate term and (ii) are respectively associated with a top-ranked document. Some embodiments consider both the sets that include the term and the sets that do not. The sets that do not include the term are assigned a numerical value for averaging that indicates that the term is not in the set. Such a weighing factor takes advantage of the fact that each set of ranked candidate terms is in fact a ranked order list. Thus, if the candidate term “Space Shuttle” appears at the top of the ranked list in many sets of candidate terms respectively associated with a top-ranked document, then it will receive a relatively high weight in this weighting scheme. Conversely, if the term “Space Shuttle” is among the last terms in each set of ranked candidate terms in which it appears, the term will receive a relatively low weight in this weighting scheme. - In some embodiments, the weight that is applied to a candidate term by
function 324 is determined in accordance with whether a term in the received query is in the candidate term. For example, if the query term was “shuttle” and the candidate term is “space shuttle”, the candidate term is given full weight, otherwise it is given no weight. - In some embodiments, the weight that is applied to a candidate term by function 324 (the weighting function) is determined in accordance with a number of characters in the candidate term. For example, the candidate term “Space Shuttle” will receive more weight than the candidate term “Dogs”.
- In some embodiments, the weight that is applied to a candidate term by
function 324 is determined in accordance with a function (e.g., average) of the rank of those top-ranked documents that are associated with a set of ranked candidate terms that includes the candidate term. Such a weighting scheme exploits the ranking that has already been applied to the initial set of search results bysearch engine 322. In such weighting schemes, candidate terms fromsets 410 associated with higher ranked documents are given precedence over candidate terms associated with lower ranked documents. For example, consider the case in which the candidate term “Space Shuttle” appears in the respective sets of ranked candidate terms associated with documents 2, 4, and 6 in the top-ranked documents in the initial group of ranked documents. Thus, in this weighting scheme, the term “Space Shuttle” will receive a weight that is a function of the value 4. Now suppose that the term “Space Shuttle” appears in the respective sets of ranked candidate terms associated with documents 10, 20, and 30 in the top-ranked documents in the initial group of ranked documents. Thus, in this weighting scheme, the term “Space Shuttle” will receive a weight that is a function of the value 20. Under this weighting scheme, the value 4 will produce a better weight (will up-weight the candidate term) relative to the weight produced with a value of 20. In some embodiments, the sets that do not include the candidate term are considered by this weighting function. They are assigned a numerical value for averaging. - In some embodiments the rank of the document in which the word first occurs as a candidate term is used in the weighting function.
- Specific weighting factors that are used by various embodiments of
selection function 326 have been outlined in order to introduce such factors. However, in preferred embodiments, several such factors are combined in order to produce desirable results. What follows are some preferred embodiments ofselection function 326. - In some embodiments, the weight that is applied to a candidate term by
function 324 is determined in accordance with any combination (or any weighted combination) of TermCount, TermPosition, ResultPosition, TermLength, and QueryInclusion, where -
- TermCount is the number of sets of ranked candidate terms that both (i) include the candidate term and (ii) are respectively associated with a top-ranked document,
- TermPosition is a function (e.g., average) of the position of the candidate term in those sets of ranked candidate terms that both (i) include the candidate term and (ii) are respectively associated with a top-ranked document,
- ResultPosition is a function (e.g., average) of the rank of those top-ranked documents that are associated with a set of ranked candidate terms that includes the candidate term,
- TermLength is a number of characters in the candidate term (candidate term complexity), and
- QueryInclusion is a value that indicates whether a term in the received query is in the candidate term.
- As used herein, application of QueryInclusion (e.g., when QueryInclusion is a non-zero value such as 1), means that the candidate term is upweighted when a term in the received query is in the candidate term. Further, nonapplication of QueryInclusion (e.g., when QueryInclusion is set equal to zero) means that the candidate term is not upweighted when a term in the received query is not in the candidate term. In some embodiments a candidate term is not credited for noise terms (e.g., a, the, who, what, where, etc.). Thus if the query includes the noise word “for” and the candidate term includes the word “for”, credit is not given to the candidate term and QueryInclusion is not upweighted.
- In some embodiments, the weight that is applied to a candidate term by
function 324 is determined in accordance with the formula:
TermCount+TermPosition+ResultPosition+TermLength+QueryInclusion.
where the weights, TermCount, TermPosition, ResultPosition, TermLength, and QueryInclusion are as defined above. In some embodiments, TermCount, TermPosition, ResultPosition, TermLength, and QueryInclusion are each independently weighted. - In some embodiments, the weight that is applied to a candidate term by
function 324 is determined in accordance with the formula:
(TermCount*w1)+(TermPosition*(w2+(RefinementDepth*w2′)))+(ResultPosition*w3)+(TermLength*(w4+(RefinementDepth*w4′)))+(QueryInclusion*(w5+(RefinementDepth*w5′)))
where w1, w2, w3, w4, w5, w2′, w4′, and w5′ are independent weights. Further, RefinementDepth is a number of times the processing has been performed for the received query. In other words, RefinementDepth is the number of times steps 602 through 612 have been repeated by operation of execution ofoptional step 614 in which a user add a term from the subset of candidate terms to the original search query. In one embodiment
w1=100
w2=15
w2=15
w3=1
w4=1
w4′=0
w5=100, and
w5′=50. - In some embodiments of the
present selection function 610 will remove certain candidate terms in sets of ranked candidate terms. For example, in some embodiments, candidate terms in the set of ranked candidate terms that only differ by a certain prefix or suffix are folded together. For example, in some embodiments, a list of prefixes and a list of suffixes are stored inmemory 314. If the only difference between two candidate terms is that one of the candidate terms includes a word that differs by a prefix at the start of a word or a suffix at the end of the word relative to the corresponding word in the other candidate term, the two candidate terms are folded together. In some embodiments, there are three classes of prefixes (and three analogous classes of suffixes). If a candidate term includes a prefix belonging to the first class, the word is discarded. If a candidate term includes a prefix that belongs to the second class, the prefix is removed. If a candidate term includes a prefix that belongs to a third class, an evaluation is performed. In this evaluation, each of the sets of ranked candidate terms associated with a top-ranked document is searched for an instance of the same term that does not include the prefix. If no such instance is found, then the prefix is not stripped. If such an instance is found then the prefix is stripped. This type of prefix (and suffix) processing is useful in many instances. For example, consider the case in which a candidate term is “the cars”. Ordinarily, the prefix “the” is considered a prefix that should be stripped. However, is could be that the candidate term is referring to a famous musical group that is typically referenced by the name “the cars”. Thus, a search ensues to see if the term “cars” without the prefix “the” is found in any of the other sets of ranked candidate terms associated with a top-ranked document. If no such instance appears, then the prefix is not stripped. In this example, it is noted that, as used here, a prefix can be a preceding affix (e.g., un-, non-, etc.) or a preceding word or phrase (e.g., the, of, to go, etc.). - In
step 612, the subset of candidate terms is presented to the user. Instep 614, the user optionally selects a term 136 (FIG. 2 ) in the subset of candidate terms and the processing (step 604), selecting (step 606) and presenting (step 612) are repeated with a revised query that includes the original (received) query and the selectedcandidate term 136 from the subset of candidate terms that was displayed in panel 140 (FIG. 2 ). As explained above, in some embodiments the user may select aterm 136 for addition to the previously submitted query, for replacement of the previously submitted query, or for use as an exclusionary term in conjunction with the previously submitted query. - All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.
- The present invention can be implemented as a computer program product that comprises a computer program mechanism embedded in a computer readable storage medium. For instance, the computer program product could contain the program modules shown in
FIG. 3 . These program modules can be stored on a CD-ROM, magnetic disk storage product, or any other computer readable data or program storage product. The software modules in the computer program product may also be distributed electronically, via the Internet or otherwise, by transmission of a computer data signal (in which the software modules are embedded) on a carrier wave. - Many modifications and variations of this invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated. The invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled.
Claims (4)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/192,724 US20060010126A1 (en) | 2003-03-21 | 2005-07-28 | Systems and methods for interactive search query refinement |
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US45690503P | 2003-03-21 | 2003-03-21 | |
US10/424,180 US6947930B2 (en) | 2003-03-21 | 2003-04-25 | Systems and methods for interactive search query refinement |
US11/192,724 US20060010126A1 (en) | 2003-03-21 | 2005-07-28 | Systems and methods for interactive search query refinement |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/424,180 Continuation US6947930B2 (en) | 2003-03-21 | 2003-04-25 | Systems and methods for interactive search query refinement |
Publications (1)
Publication Number | Publication Date |
---|---|
US20060010126A1 true US20060010126A1 (en) | 2006-01-12 |
Family
ID=32993957
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/424,180 Expired - Lifetime US6947930B2 (en) | 2003-03-21 | 2003-04-25 | Systems and methods for interactive search query refinement |
US11/192,724 Abandoned US20060010126A1 (en) | 2003-03-21 | 2005-07-28 | Systems and methods for interactive search query refinement |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/424,180 Expired - Lifetime US6947930B2 (en) | 2003-03-21 | 2003-04-25 | Systems and methods for interactive search query refinement |
Country Status (5)
Country | Link |
---|---|
US (2) | US6947930B2 (en) |
EP (1) | EP1606704A4 (en) |
JP (4) | JP5255766B2 (en) |
KR (1) | KR100666064B1 (en) |
WO (1) | WO2004086192A2 (en) |
Cited By (42)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050210049A1 (en) * | 2004-03-22 | 2005-09-22 | Sliccware | Secure virtual data warehousing system and method |
US20060010117A1 (en) * | 2004-07-06 | 2006-01-12 | Icosystem Corporation | Methods and systems for interactive search |
US20060161520A1 (en) * | 2005-01-14 | 2006-07-20 | Microsoft Corporation | System and method for generating alternative search terms |
US20070027848A1 (en) * | 2005-07-29 | 2007-02-01 | Microsoft Corporation | Smart search for accessing options |
US20070033178A1 (en) * | 2005-06-23 | 2007-02-08 | Cognos Incorporated | Quality of service feedback for technology-neutral data reporting |
US20070067212A1 (en) * | 2005-09-21 | 2007-03-22 | Eric Bonabeau | System and method for aiding product design and quantifying acceptance |
US20070208738A1 (en) * | 2006-03-03 | 2007-09-06 | Morgan Brian S | Techniques for providing suggestions for creating a search query |
US20070271255A1 (en) * | 2006-05-17 | 2007-11-22 | Nicky Pappo | Reverse search-engine |
US20070288451A1 (en) * | 2006-06-07 | 2007-12-13 | Marek Meyer | Generating searchable keywords |
US20080134033A1 (en) * | 2006-11-30 | 2008-06-05 | Microsoft Corporation | Rank graph |
US20080281857A1 (en) * | 2007-05-10 | 2008-11-13 | Xerox Corporation | Event hierarchies and memory organization for structured data retrieval |
US20090019034A1 (en) * | 2007-06-26 | 2009-01-15 | Seeqpod, Inc. | Media discovery and playlist generation |
US20090100039A1 (en) * | 2007-10-11 | 2009-04-16 | Oracle International Corp | Extensible mechanism for grouping search results |
US20090106241A1 (en) * | 2005-04-19 | 2009-04-23 | International Business Machines Corporation | Search criteria control system and method |
US20090132500A1 (en) * | 2007-11-21 | 2009-05-21 | Chacha Search, Inc. | Method and system for improving utilization of human searchers |
US20090192985A1 (en) * | 2008-01-30 | 2009-07-30 | International Business Machines Corporation | Method, system, and program product for enhanced search query modification |
US20100114933A1 (en) * | 2008-10-24 | 2010-05-06 | Vanessa Murdock | Methods for improving the diversity of image search results |
US20100125568A1 (en) * | 2008-11-18 | 2010-05-20 | Van Zwol Roelof | Dynamic feature weighting |
US20100211558A1 (en) * | 2004-07-06 | 2010-08-19 | Icosystem Corporation | Methods and apparatus for interactive searching techniques |
US20100299343A1 (en) * | 2009-05-22 | 2010-11-25 | Microsoft Corporation | Identifying Task Groups for Organizing Search Results |
US20110040778A1 (en) * | 2006-07-18 | 2011-02-17 | Webex Communications, Inc. | Methods and apparatuses for dynamically displaying search suggestions |
US20110137912A1 (en) * | 2009-12-08 | 2011-06-09 | International Business Machines Corporation | System, method and computer program product for documents retrieval |
US8117139B2 (en) | 2003-04-04 | 2012-02-14 | Icosystem Corporation | Methods and systems for interactive evolutionary computing (IEC) |
US8117140B2 (en) | 2003-08-01 | 2012-02-14 | Icosystem Corporation | Methods and systems for applying genetic operators to determine systems conditions |
US20120110453A1 (en) * | 2010-10-29 | 2012-05-03 | Microsoft Corporation | Display of Image Search Results |
US20120158765A1 (en) * | 2010-12-15 | 2012-06-21 | Microsoft Corporation | User Interface for Interactive Query Reformulation |
WO2013016288A1 (en) * | 2011-07-27 | 2013-01-31 | Microsoft Corporation | Utilization of features extracted from structured documents to improve search relevance |
US8515935B1 (en) | 2007-05-31 | 2013-08-20 | Google Inc. | Identifying related queries |
US8566340B2 (en) * | 2011-12-07 | 2013-10-22 | Microsoft Corporation | Provision of query suggestions independent of query logs |
US8849785B1 (en) | 2010-01-15 | 2014-09-30 | Google Inc. | Search query reformulation using result term occurrence count |
US8918417B1 (en) * | 2009-06-05 | 2014-12-23 | Google Inc. | Generating query refinements from user preference data |
US8930350B1 (en) | 2009-03-23 | 2015-01-06 | Google Inc. | Autocompletion using previously submitted query data |
US9092528B1 (en) | 2009-08-28 | 2015-07-28 | Google Inc. | Providing result-based query suggestions |
US20150278366A1 (en) * | 2011-06-03 | 2015-10-01 | Google Inc. | Identifying topical entities |
US9183323B1 (en) | 2008-06-27 | 2015-11-10 | Google Inc. | Suggesting alternative query phrases in query results |
US9251185B2 (en) | 2010-12-15 | 2016-02-02 | Girish Kumar | Classifying results of search queries |
US9767144B2 (en) | 2012-04-20 | 2017-09-19 | Microsoft Technology Licensing, Llc | Search system with query refinement |
US20170270159A1 (en) * | 2013-03-14 | 2017-09-21 | Google Inc. | Determining query results in response to natural language queries |
US10108699B2 (en) | 2013-01-22 | 2018-10-23 | Microsoft Technology Licensing, Llc | Adaptive query suggestion |
US11360958B2 (en) | 2017-09-29 | 2022-06-14 | Apple Inc. | Techniques for indexing and querying a set of documents at a computing device |
WO2022231646A1 (en) * | 2021-04-30 | 2022-11-03 | CS Disco, Inc. | Systems and methods for searching related documents and associated search operators |
US20230032882A1 (en) * | 2015-09-04 | 2023-02-02 | Palantir Technologies Inc. | Systems and methods for database investigation tool |
Families Citing this family (291)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8352400B2 (en) | 1991-12-23 | 2013-01-08 | Hoffberg Steven M | Adaptive pattern recognition based controller apparatus and method and human-factored interface therefore |
US7966078B2 (en) | 1999-02-01 | 2011-06-21 | Steven Hoffberg | Network media appliance system and method |
US6883135B1 (en) | 2000-01-28 | 2005-04-19 | Microsoft Corporation | Proxy server using a statistical model |
US20010053991A1 (en) * | 2000-03-08 | 2001-12-20 | Bonabeau Eric W. | Methods and systems for generating business models |
US7035864B1 (en) | 2000-05-18 | 2006-04-25 | Endeca Technologies, Inc. | Hierarchical data-driven navigation system and method for information retrieval |
US7617184B2 (en) * | 2000-05-18 | 2009-11-10 | Endeca Technologies, Inc. | Scalable hierarchical data-driven navigation system and method for information retrieval |
WO2003038749A1 (en) * | 2001-10-31 | 2003-05-08 | Icosystem Corporation | Method and system for implementing evolutionary algorithms |
US8590013B2 (en) | 2002-02-25 | 2013-11-19 | C. S. Lee Crawford | Method of managing and communicating data pertaining to software applications for processor-based devices comprising wireless communication circuitry |
US20070038614A1 (en) * | 2005-08-10 | 2007-02-15 | Guha Ramanathan V | Generating and presenting advertisements based on context data for programmable search engines |
US7693830B2 (en) | 2005-08-10 | 2010-04-06 | Google Inc. | Programmable search engine |
US20040117366A1 (en) * | 2002-12-12 | 2004-06-17 | Ferrari Adam J. | Method and system for interpreting multiple-term queries |
US20050038781A1 (en) * | 2002-12-12 | 2005-02-17 | Endeca Technologies, Inc. | Method and system for interpreting multiple-term queries |
US8375008B1 (en) | 2003-01-17 | 2013-02-12 | Robert Gomes | Method and system for enterprise-wide retention of digital or electronic data |
US8943024B1 (en) | 2003-01-17 | 2015-01-27 | Daniel John Gardner | System and method for data de-duplication |
US8630984B1 (en) | 2003-01-17 | 2014-01-14 | Renew Data Corp. | System and method for data extraction from email files |
US8065277B1 (en) | 2003-01-17 | 2011-11-22 | Daniel John Gardner | System and method for a data extraction and backup database |
US6947930B2 (en) * | 2003-03-21 | 2005-09-20 | Overture Services, Inc. | Systems and methods for interactive search query refinement |
KR101123426B1 (en) | 2003-04-04 | 2012-03-23 | 야후! 인크. | A system for generating search results including searching by subdomain hints and providing sponsored results by subdomain |
US7340480B2 (en) * | 2003-05-08 | 2008-03-04 | International Business Machines Corporation | Iterative data analysis enabled through query result abstraction |
US7260571B2 (en) * | 2003-05-19 | 2007-08-21 | International Business Machines Corporation | Disambiguation of term occurrences |
US7401072B2 (en) * | 2003-06-10 | 2008-07-15 | Google Inc. | Named URL entry |
US7228301B2 (en) * | 2003-06-27 | 2007-06-05 | Microsoft Corporation | Method for normalizing document metadata to improve search results using an alias relationship directory service |
GB2403636A (en) * | 2003-07-02 | 2005-01-05 | Sony Uk Ltd | Information retrieval using an array of nodes |
US7627613B1 (en) * | 2003-07-03 | 2009-12-01 | Google Inc. | Duplicate document detection in a web crawler system |
US8136025B1 (en) | 2003-07-03 | 2012-03-13 | Google Inc. | Assigning document identification tags |
US7428700B2 (en) * | 2003-07-28 | 2008-09-23 | Microsoft Corporation | Vision-based document segmentation |
US8856163B2 (en) * | 2003-07-28 | 2014-10-07 | Google Inc. | System and method for providing a user interface with search query broadening |
US7617203B2 (en) * | 2003-08-01 | 2009-11-10 | Yahoo! Inc | Listings optimization using a plurality of data sources |
US8869061B1 (en) | 2003-08-29 | 2014-10-21 | Microsoft Corporation | User interface for searching an electronic document |
US7617205B2 (en) | 2005-03-30 | 2009-11-10 | Google Inc. | Estimating confidence for query revision models |
US7590936B1 (en) | 2003-09-30 | 2009-09-15 | Microsoft Corporation | Method for extracting information associated with a search term |
US7231399B1 (en) | 2003-11-14 | 2007-06-12 | Google Inc. | Ranking documents based on large data sets |
US7844589B2 (en) * | 2003-11-18 | 2010-11-30 | Yahoo! Inc. | Method and apparatus for performing a search |
US7890526B1 (en) * | 2003-12-30 | 2011-02-15 | Microsoft Corporation | Incremental query refinement |
US7707039B2 (en) | 2004-02-15 | 2010-04-27 | Exbiblio B.V. | Automatic modification of web pages |
US8442331B2 (en) | 2004-02-15 | 2013-05-14 | Google Inc. | Capturing text from rendered documents using supplemental information |
US20050182755A1 (en) * | 2004-02-14 | 2005-08-18 | Bao Tran | Systems and methods for analyzing documents over a network |
US7812860B2 (en) | 2004-04-01 | 2010-10-12 | Exbiblio B.V. | Handheld device for capturing text from both a document printed on paper and a document displayed on a dynamic display device |
US10635723B2 (en) | 2004-02-15 | 2020-04-28 | Google Llc | Search engines and systems with handheld document data capture devices |
US20050210003A1 (en) * | 2004-03-17 | 2005-09-22 | Yih-Kuen Tsay | Sequence based indexing and retrieval method for text documents |
US7584221B2 (en) | 2004-03-18 | 2009-09-01 | Microsoft Corporation | Field weighting in text searching |
US9143638B2 (en) | 2004-04-01 | 2015-09-22 | Google Inc. | Data capture from rendered documents using handheld device |
US20060081714A1 (en) | 2004-08-23 | 2006-04-20 | King Martin T | Portable scanning device |
US9008447B2 (en) | 2004-04-01 | 2015-04-14 | Google Inc. | Method and system for character recognition |
US7990556B2 (en) | 2004-12-03 | 2011-08-02 | Google Inc. | Association of a portable scanner with input/output and storage devices |
US8081849B2 (en) | 2004-12-03 | 2011-12-20 | Google Inc. | Portable scanning and memory device |
US9116890B2 (en) | 2004-04-01 | 2015-08-25 | Google Inc. | Triggering actions in response to optically or acoustically capturing keywords from a rendered document |
US8146156B2 (en) | 2004-04-01 | 2012-03-27 | Google Inc. | Archive of text captures from rendered documents |
US7894670B2 (en) | 2004-04-01 | 2011-02-22 | Exbiblio B.V. | Triggering actions in response to optically or acoustically capturing keywords from a rendered document |
US20060098900A1 (en) | 2004-09-27 | 2006-05-11 | King Martin T | Secure data gathering from rendered documents |
US8713418B2 (en) | 2004-04-12 | 2014-04-29 | Google Inc. | Adding value to a rendered document |
US8874504B2 (en) | 2004-12-03 | 2014-10-28 | Google Inc. | Processing techniques for visual capture data from a rendered document |
US8620083B2 (en) | 2004-12-03 | 2013-12-31 | Google Inc. | Method and system for character recognition |
US8489624B2 (en) | 2004-05-17 | 2013-07-16 | Google, Inc. | Processing techniques for text capture from a rendered document |
US7716225B1 (en) | 2004-06-17 | 2010-05-11 | Google Inc. | Ranking documents based on user behavior and/or feature data |
US9223868B2 (en) | 2004-06-28 | 2015-12-29 | Google Inc. | Deriving and using interaction profiles |
US8346620B2 (en) | 2004-07-19 | 2013-01-01 | Google Inc. | Automatic modification of web pages |
US7702618B1 (en) | 2004-07-26 | 2010-04-20 | Google Inc. | Information retrieval system for archiving multiple document versions |
US7580929B2 (en) * | 2004-07-26 | 2009-08-25 | Google Inc. | Phrase-based personalization of searches in an information retrieval system |
US7584175B2 (en) | 2004-07-26 | 2009-09-01 | Google Inc. | Phrase-based generation of document descriptions |
US7567959B2 (en) * | 2004-07-26 | 2009-07-28 | Google Inc. | Multiple index based information retrieval system |
US7536408B2 (en) | 2004-07-26 | 2009-05-19 | Google Inc. | Phrase-based indexing in an information retrieval system |
US7711679B2 (en) | 2004-07-26 | 2010-05-04 | Google Inc. | Phrase-based detection of duplicate documents in an information retrieval system |
US7426507B1 (en) * | 2004-07-26 | 2008-09-16 | Google, Inc. | Automatic taxonomy generation in search results using phrases |
US7580921B2 (en) | 2004-07-26 | 2009-08-25 | Google Inc. | Phrase identification in an information retrieval system |
US7599914B2 (en) * | 2004-07-26 | 2009-10-06 | Google Inc. | Phrase-based searching in an information retrieval system |
US7199571B2 (en) * | 2004-07-27 | 2007-04-03 | Optisense Network, Inc. | Probe apparatus for use in a separable connector, and systems including same |
US8819051B2 (en) * | 2005-09-29 | 2014-08-26 | Yahoo! Inc. | Tagging offline content with context-sensitive search-enabling keywords |
US20070016559A1 (en) * | 2005-07-14 | 2007-01-18 | Yahoo! Inc. | User entertainment and engagement enhancements to search system |
US7421441B1 (en) * | 2005-09-20 | 2008-09-02 | Yahoo! Inc. | Systems and methods for presenting information based on publisher-selected labels |
US7958115B2 (en) * | 2004-07-29 | 2011-06-07 | Yahoo! Inc. | Search systems and methods using in-line contextual queries |
US7603349B1 (en) | 2004-07-29 | 2009-10-13 | Yahoo! Inc. | User interfaces for search systems using in-line contextual queries |
US7962465B2 (en) * | 2006-10-19 | 2011-06-14 | Yahoo! Inc. | Contextual syndication platform |
US8972856B2 (en) * | 2004-07-29 | 2015-03-03 | Yahoo! Inc. | Document modification by a client-side application |
US7917480B2 (en) * | 2004-08-13 | 2011-03-29 | Google Inc. | Document compression system and method for use with tokenspace repository |
US7275052B2 (en) * | 2004-08-20 | 2007-09-25 | Sap Ag | Combined classification based on examples, queries, and keywords |
WO2006031741A2 (en) * | 2004-09-10 | 2006-03-23 | Topixa, Inc. | User creating and rating of attachments for conducting a search directed by a hierarchy-free set of topics, and a user interface therefor |
US7606793B2 (en) | 2004-09-27 | 2009-10-20 | Microsoft Corporation | System and method for scoping searches using index keys |
US7761448B2 (en) | 2004-09-30 | 2010-07-20 | Microsoft Corporation | System and method for ranking search results using click distance |
US7739277B2 (en) | 2004-09-30 | 2010-06-15 | Microsoft Corporation | System and method for incorporating anchor text into ranking search results |
US7827181B2 (en) | 2004-09-30 | 2010-11-02 | Microsoft Corporation | Click distance determination |
US8065316B1 (en) * | 2004-09-30 | 2011-11-22 | Google Inc. | Systems and methods for providing search query refinements |
US8069151B1 (en) | 2004-12-08 | 2011-11-29 | Chris Crafford | System and method for detecting incongruous or incorrect media in a data recovery process |
US20060129531A1 (en) * | 2004-12-09 | 2006-06-15 | International Business Machines Corporation | Method and system for suggesting search engine keywords |
US7716198B2 (en) | 2004-12-21 | 2010-05-11 | Microsoft Corporation | Ranking search results using feature extraction |
US7630980B2 (en) | 2005-01-21 | 2009-12-08 | Prashant Parikh | Automatic dynamic contextual data entry completion system |
WO2006086179A2 (en) * | 2005-01-31 | 2006-08-17 | Textdigger, Inc. | Method and system for semantic search and retrieval of electronic documents |
US7890503B2 (en) * | 2005-02-07 | 2011-02-15 | Microsoft Corporation | Method and system for performing secondary search actions based on primary search result attributes |
US8527468B1 (en) | 2005-02-08 | 2013-09-03 | Renew Data Corp. | System and method for management of retention periods for content in a computing system |
US7461059B2 (en) | 2005-02-23 | 2008-12-02 | Microsoft Corporation | Dynamically updated search results based upon continuously-evolving search query that is based at least in part upon phrase suggestion, search engine uses previous result sets performing additional search tasks |
WO2006094151A2 (en) * | 2005-03-01 | 2006-09-08 | Adapt Technologies Inc., | Query-less searching |
US7792833B2 (en) | 2005-03-03 | 2010-09-07 | Microsoft Corporation | Ranking search results using language types |
US7526476B2 (en) * | 2005-03-14 | 2009-04-28 | Microsoft Corporation | System and method for generating attribute-based selectable search extension |
US7870147B2 (en) * | 2005-03-29 | 2011-01-11 | Google Inc. | Query revision using known highly-ranked queries |
US7565345B2 (en) * | 2005-03-29 | 2009-07-21 | Google Inc. | Integration of multiple query revision models |
WO2006110684A2 (en) * | 2005-04-11 | 2006-10-19 | Textdigger, Inc. | System and method for searching for a query |
US20060248078A1 (en) * | 2005-04-15 | 2006-11-02 | William Gross | Search engine with suggestion tool and method of using same |
US20060248037A1 (en) * | 2005-04-29 | 2006-11-02 | International Business Machines Corporation | Annotation of inverted list text indexes using search queries |
US8438142B2 (en) * | 2005-05-04 | 2013-05-07 | Google Inc. | Suggesting and refining user input based on original user input |
EP1889181A4 (en) * | 2005-05-16 | 2009-12-02 | Ebay Inc | Method and system to process a data search request |
US7962504B1 (en) * | 2005-05-26 | 2011-06-14 | Aol Inc. | Sourcing terms into a search engine |
US20070016545A1 (en) * | 2005-07-14 | 2007-01-18 | International Business Machines Corporation | Detection of missing content in a searchable repository |
US7599917B2 (en) | 2005-08-15 | 2009-10-06 | Microsoft Corporation | Ranking search results using biased click distance |
US7747639B2 (en) * | 2005-08-24 | 2010-06-29 | Yahoo! Inc. | Alternative search query prediction |
US7844599B2 (en) * | 2005-08-24 | 2010-11-30 | Yahoo! Inc. | Biasing queries to determine suggested queries |
US7672932B2 (en) * | 2005-08-24 | 2010-03-02 | Yahoo! Inc. | Speculative search result based on a not-yet-submitted search query |
JP4756953B2 (en) * | 2005-08-26 | 2011-08-24 | 富士通株式会社 | Information search apparatus and information search method |
US9201979B2 (en) | 2005-09-14 | 2015-12-01 | Millennial Media, Inc. | Syndication of a behavioral profile associated with an availability condition using a monetization platform |
US7577665B2 (en) | 2005-09-14 | 2009-08-18 | Jumptap, Inc. | User characteristic influenced search results |
US9076175B2 (en) | 2005-09-14 | 2015-07-07 | Millennial Media, Inc. | Mobile comparison shopping |
US9471925B2 (en) | 2005-09-14 | 2016-10-18 | Millennial Media Llc | Increasing mobile interactivity |
US7752209B2 (en) | 2005-09-14 | 2010-07-06 | Jumptap, Inc. | Presenting sponsored content on a mobile communication facility |
US8805339B2 (en) | 2005-09-14 | 2014-08-12 | Millennial Media, Inc. | Categorization of a mobile user profile based on browse and viewing behavior |
US7603360B2 (en) | 2005-09-14 | 2009-10-13 | Jumptap, Inc. | Location influenced search results |
US8463249B2 (en) | 2005-09-14 | 2013-06-11 | Jumptap, Inc. | System for targeting advertising content to a plurality of mobile communication facilities |
JP2009508273A (en) * | 2005-09-14 | 2009-02-26 | オー−ヤ!,インク. | Apparatus and method for indexing and searching networked information |
US8156128B2 (en) | 2005-09-14 | 2012-04-10 | Jumptap, Inc. | Contextual mobile content placement on a mobile communication facility |
US10038756B2 (en) | 2005-09-14 | 2018-07-31 | Millenial Media LLC | Managing sponsored content based on device characteristics |
US8364521B2 (en) | 2005-09-14 | 2013-01-29 | Jumptap, Inc. | Rendering targeted advertisement on mobile communication facilities |
US8027879B2 (en) | 2005-11-05 | 2011-09-27 | Jumptap, Inc. | Exclusivity bidding for mobile sponsored content |
US8503995B2 (en) | 2005-09-14 | 2013-08-06 | Jumptap, Inc. | Mobile dynamic advertisement creation and placement |
US8238888B2 (en) | 2006-09-13 | 2012-08-07 | Jumptap, Inc. | Methods and systems for mobile coupon placement |
US8615719B2 (en) | 2005-09-14 | 2013-12-24 | Jumptap, Inc. | Managing sponsored content for delivery to mobile communication facilities |
US8688671B2 (en) | 2005-09-14 | 2014-04-01 | Millennial Media | Managing sponsored content based on geographic region |
US8103545B2 (en) | 2005-09-14 | 2012-01-24 | Jumptap, Inc. | Managing payment for sponsored content presented to mobile communication facilities |
US8660891B2 (en) | 2005-11-01 | 2014-02-25 | Millennial Media | Interactive mobile advertisement banners |
US7676394B2 (en) | 2005-09-14 | 2010-03-09 | Jumptap, Inc. | Dynamic bidding and expected value |
US7548915B2 (en) | 2005-09-14 | 2009-06-16 | Jorey Ramer | Contextual mobile content placement on a mobile communication facility |
US20110313853A1 (en) | 2005-09-14 | 2011-12-22 | Jorey Ramer | System for targeting advertising content to a plurality of mobile communication facilities |
US8131271B2 (en) | 2005-11-05 | 2012-03-06 | Jumptap, Inc. | Categorization of a mobile user profile based on browse behavior |
US8195133B2 (en) | 2005-09-14 | 2012-06-05 | Jumptap, Inc. | Mobile dynamic advertisement creation and placement |
US7769764B2 (en) | 2005-09-14 | 2010-08-03 | Jumptap, Inc. | Mobile advertisement syndication |
US8989718B2 (en) | 2005-09-14 | 2015-03-24 | Millennial Media, Inc. | Idle screen advertising |
US8209344B2 (en) | 2005-09-14 | 2012-06-26 | Jumptap, Inc. | Embedding sponsored content in mobile applications |
US7912458B2 (en) | 2005-09-14 | 2011-03-22 | Jumptap, Inc. | Interaction analysis and prioritization of mobile content |
US8229914B2 (en) | 2005-09-14 | 2012-07-24 | Jumptap, Inc. | Mobile content spidering and compatibility determination |
US9058406B2 (en) | 2005-09-14 | 2015-06-16 | Millennial Media, Inc. | Management of multiple advertising inventories using a monetization platform |
US8666376B2 (en) | 2005-09-14 | 2014-03-04 | Millennial Media | Location based mobile shopping affinity program |
US8812526B2 (en) | 2005-09-14 | 2014-08-19 | Millennial Media, Inc. | Mobile content cross-inventory yield optimization |
US10592930B2 (en) | 2005-09-14 | 2020-03-17 | Millenial Media, LLC | Syndication of a behavioral profile using a monetization platform |
US8302030B2 (en) | 2005-09-14 | 2012-10-30 | Jumptap, Inc. | Management of multiple advertising inventories using a monetization platform |
US8832100B2 (en) | 2005-09-14 | 2014-09-09 | Millennial Media, Inc. | User transaction history influenced search results |
US7702318B2 (en) | 2005-09-14 | 2010-04-20 | Jumptap, Inc. | Presentation of sponsored content based on mobile transaction event |
US9703892B2 (en) | 2005-09-14 | 2017-07-11 | Millennial Media Llc | Predictive text completion for a mobile communication facility |
US8819659B2 (en) | 2005-09-14 | 2014-08-26 | Millennial Media, Inc. | Mobile search service instant activation |
US8290810B2 (en) | 2005-09-14 | 2012-10-16 | Jumptap, Inc. | Realtime surveying within mobile sponsored content |
US7860871B2 (en) | 2005-09-14 | 2010-12-28 | Jumptap, Inc. | User history influenced search results |
US8311888B2 (en) | 2005-09-14 | 2012-11-13 | Jumptap, Inc. | Revenue models associated with syndication of a behavioral profile using a monetization platform |
US8364540B2 (en) | 2005-09-14 | 2013-01-29 | Jumptap, Inc. | Contextual targeting of content using a monetization platform |
US10911894B2 (en) | 2005-09-14 | 2021-02-02 | Verizon Media Inc. | Use of dynamic content generation parameters based on previous performance of those parameters |
US7660581B2 (en) | 2005-09-14 | 2010-02-09 | Jumptap, Inc. | Managing sponsored content based on usage history |
EP1938223A4 (en) * | 2005-09-29 | 2009-11-11 | Icosystem Corp | Methods and apparatus for interactive searching techniques |
US7480652B2 (en) * | 2005-10-26 | 2009-01-20 | Microsoft Corporation | Determining relevance of a document to a query based on spans of query terms |
US8175585B2 (en) | 2005-11-05 | 2012-05-08 | Jumptap, Inc. | System for targeting advertising content to a plurality of mobile communication facilities |
US8019752B2 (en) * | 2005-11-10 | 2011-09-13 | Endeca Technologies, Inc. | System and method for information retrieval from object collections with complex interrelationships |
US8571999B2 (en) | 2005-11-14 | 2013-10-29 | C. S. Lee Crawford | Method of conducting operations for a social network application including activity list generation |
US20070143255A1 (en) * | 2005-11-28 | 2007-06-21 | Webaroo, Inc. | Method and system for delivering internet content to mobile devices |
US7668887B2 (en) * | 2005-12-01 | 2010-02-23 | Object Positive Pty Ltd | Method, system and software product for locating documents of interest |
US8903810B2 (en) | 2005-12-05 | 2014-12-02 | Collarity, Inc. | Techniques for ranking search results |
US8429184B2 (en) * | 2005-12-05 | 2013-04-23 | Collarity Inc. | Generation of refinement terms for search queries |
US7925649B2 (en) | 2005-12-30 | 2011-04-12 | Google Inc. | Method, system, and graphical user interface for alerting a computer user to new results for a prior search |
WO2007081681A2 (en) | 2006-01-03 | 2007-07-19 | Textdigger, Inc. | Search system with query refinement and search method |
US20070185860A1 (en) * | 2006-01-24 | 2007-08-09 | Michael Lissack | System for searching |
US20070192293A1 (en) * | 2006-02-13 | 2007-08-16 | Bing Swen | Method for presenting search results |
US20070208733A1 (en) * | 2006-02-22 | 2007-09-06 | Copernic Technologies, Inc. | Query Correction Using Indexed Content on a Desktop Indexer Program |
US8195683B2 (en) | 2006-02-28 | 2012-06-05 | Ebay Inc. | Expansion of database search queries |
US7689554B2 (en) * | 2006-02-28 | 2010-03-30 | Yahoo! Inc. | System and method for identifying related queries for languages with multiple writing systems |
US7657523B2 (en) * | 2006-03-09 | 2010-02-02 | Customerforce.Com | Ranking search results presented to on-line users as a function of perspectives of relationships trusted by the users |
WO2007114932A2 (en) | 2006-04-04 | 2007-10-11 | Textdigger, Inc. | Search system and method with text function tagging |
EP2013780A4 (en) * | 2006-04-13 | 2009-05-13 | Searchme Inc | Systems and methods for performing searches within vertical domains |
JP4761460B2 (en) * | 2006-05-01 | 2011-08-31 | コニカミノルタビジネステクノロジーズ株式会社 | Information search method, information search device, and information search processing program by search device |
US9443022B2 (en) | 2006-06-05 | 2016-09-13 | Google Inc. | Method, system, and graphical user interface for providing personalized recommendations of popular search queries |
US8150827B2 (en) * | 2006-06-07 | 2012-04-03 | Renew Data Corp. | Methods for enhancing efficiency and cost effectiveness of first pass review of documents |
US20080189273A1 (en) * | 2006-06-07 | 2008-08-07 | Digital Mandate, Llc | System and method for utilizing advanced search and highlighting techniques for isolating subsets of relevant content data |
CA2652150A1 (en) * | 2006-06-13 | 2007-12-21 | Microsoft Corporation | Search engine dash-board |
US7761464B2 (en) * | 2006-06-19 | 2010-07-20 | Microsoft Corporation | Diversifying search results for improved search and personalization |
US7991769B2 (en) * | 2006-07-07 | 2011-08-02 | Yahoo! Inc. | System and method for budgeted generalization search in hierarchies |
US20080010250A1 (en) * | 2006-07-07 | 2008-01-10 | Yahoo! Inc. | System and method for generalization search in hierarchies |
US8301616B2 (en) * | 2006-07-14 | 2012-10-30 | Yahoo! Inc. | Search equalizer |
US8001114B2 (en) * | 2006-07-18 | 2011-08-16 | Wilson Chu | Methods and apparatuses for dynamically searching for electronic mail messages |
EP2067119A2 (en) | 2006-09-08 | 2009-06-10 | Exbiblio B.V. | Optical scanners, such as hand-held optical scanners |
US7761805B2 (en) | 2006-09-11 | 2010-07-20 | Yahoo! Inc. | Displaying items using a reduced presentation |
US8442972B2 (en) | 2006-10-11 | 2013-05-14 | Collarity, Inc. | Negative associations for search results ranking and refinement |
US20080109274A1 (en) * | 2006-11-03 | 2008-05-08 | Yahoo! Inc. | System and method for predicting a casing variation of a term |
US8635203B2 (en) * | 2006-11-16 | 2014-01-21 | Yahoo! Inc. | Systems and methods using query patterns to disambiguate query intent |
US8131722B2 (en) * | 2006-11-20 | 2012-03-06 | Ebay Inc. | Search clustering |
US7840076B2 (en) * | 2006-11-22 | 2010-11-23 | Intel Corporation | Methods and apparatus for retrieving images from a large collection of images |
US8676802B2 (en) | 2006-11-30 | 2014-03-18 | Oracle Otc Subsidiary Llc | Method and system for information retrieval with clustering |
US7921092B2 (en) * | 2006-12-04 | 2011-04-05 | Yahoo! Inc. | Topic-focused search result summaries |
US20080154878A1 (en) * | 2006-12-20 | 2008-06-26 | Rose Daniel E | Diversifying a set of items |
US7792816B2 (en) | 2007-02-01 | 2010-09-07 | Icosystem Corporation | Method and system for fast, generic, online and offline, multi-source text analysis and visualization |
US7925644B2 (en) * | 2007-03-01 | 2011-04-12 | Microsoft Corporation | Efficient retrieval algorithm by query term discrimination |
US8166021B1 (en) | 2007-03-30 | 2012-04-24 | Google Inc. | Query phrasification |
US7702614B1 (en) | 2007-03-30 | 2010-04-20 | Google Inc. | Index updating using segment swapping |
US8166045B1 (en) | 2007-03-30 | 2012-04-24 | Google Inc. | Phrase extraction using subphrase scoring |
US7925655B1 (en) | 2007-03-30 | 2011-04-12 | Google Inc. | Query scheduling using hierarchical tiers of index servers |
US8086594B1 (en) | 2007-03-30 | 2011-12-27 | Google Inc. | Bifurcated document relevance scoring |
US7693813B1 (en) | 2007-03-30 | 2010-04-06 | Google Inc. | Index server architecture using tiered and sharded phrase posting lists |
US20080250008A1 (en) * | 2007-04-04 | 2008-10-09 | Microsoft Corporation | Query Specialization |
US20080256056A1 (en) * | 2007-04-10 | 2008-10-16 | Yahoo! Inc. | System for building a data structure representing a network of users and advertisers |
US8261200B2 (en) * | 2007-04-26 | 2012-09-04 | Fuji Xerox Co., Ltd. | Increasing retrieval performance of images by providing relevance feedback on word images contained in the images |
US7809714B1 (en) | 2007-04-30 | 2010-10-05 | Lawrence Richard Smith | Process for enhancing queries for information retrieval |
US20080294619A1 (en) * | 2007-05-23 | 2008-11-27 | Hamilton Ii Rick Allen | System and method for automatic generation of search suggestions based on recent operator behavior |
US8392446B2 (en) * | 2007-05-31 | 2013-03-05 | Yahoo! Inc. | System and method for providing vector terms related to a search query |
US8713001B2 (en) * | 2007-07-10 | 2014-04-29 | Asim Roy | Systems and related methods of user-guided searching |
GB2454161A (en) * | 2007-08-15 | 2009-05-06 | Transversal Corp Ltd | A mechanism for improving the effectiveness of an internet search engine |
US8117223B2 (en) | 2007-09-07 | 2012-02-14 | Google Inc. | Integrating external related phrase information into a phrase-based indexing information retrieval system |
CN101842787A (en) * | 2007-09-14 | 2010-09-22 | 谷歌公司 | Suggesting alterntive queries in query results |
US8638363B2 (en) | 2009-02-18 | 2014-01-28 | Google Inc. | Automatically capturing information, such as capturing information using a document-aware device |
US9348912B2 (en) | 2007-10-18 | 2016-05-24 | Microsoft Technology Licensing, Llc | Document length as a static relevance feature for ranking search results |
US7840569B2 (en) | 2007-10-18 | 2010-11-23 | Microsoft Corporation | Enterprise relevancy ranking using a neural network |
CN101159967B (en) * | 2007-10-29 | 2011-08-31 | 中国移动通信集团设计院有限公司 | Method and device for using drive test data for propagation model revision |
WO2009059297A1 (en) * | 2007-11-01 | 2009-05-07 | Textdigger, Inc. | Method and apparatus for automated tag generation for digital content |
US20090150387A1 (en) * | 2007-11-08 | 2009-06-11 | Marchewitz Jodi L | Guided research tool |
US7856434B2 (en) | 2007-11-12 | 2010-12-21 | Endeca Technologies, Inc. | System and method for filtering rules for manipulating search results in a hierarchical search and navigation system |
US20090171907A1 (en) * | 2007-12-26 | 2009-07-02 | Radovanovic Nash R | Method and system for searching text-containing documents |
US20090171929A1 (en) * | 2007-12-26 | 2009-07-02 | Microsoft Corporation | Toward optimized query suggeston: user interfaces and algorithms |
US8255386B1 (en) * | 2008-01-30 | 2012-08-28 | Google Inc. | Selection of documents to place in search index |
US8615490B1 (en) | 2008-01-31 | 2013-12-24 | Renew Data Corp. | Method and system for restoring information from backup storage media |
US7930287B2 (en) * | 2008-03-14 | 2011-04-19 | Michelli Capital Limited Liability Company | Systems and methods for compound searching |
US20090241058A1 (en) * | 2008-03-18 | 2009-09-24 | Cuill, Inc. | Apparatus and method for displaying search results with an associated anchor area |
KR100926876B1 (en) * | 2008-04-01 | 2009-11-16 | 엔에이치엔(주) | Rank Learning Model Creation Method and Rank Learning Model Creation System Using Rank Occurrence Probability |
US8812493B2 (en) | 2008-04-11 | 2014-08-19 | Microsoft Corporation | Search results ranking using editing distance and document information |
US8051080B2 (en) * | 2008-04-16 | 2011-11-01 | Yahoo! Inc. | Contextual ranking of keywords using click data |
US8086590B2 (en) * | 2008-04-25 | 2011-12-27 | Microsoft Corporation | Product suggestions and bypassing irrelevant query results |
US8082248B2 (en) * | 2008-05-29 | 2011-12-20 | Rania Abouyounes | Method and system for document classification based on document structure and written style |
US8438178B2 (en) | 2008-06-26 | 2013-05-07 | Collarity Inc. | Interactions among online digital identities |
US8521731B2 (en) | 2008-07-09 | 2013-08-27 | Yahoo! Inc. | Systems and methods for query expansion in sponsored search |
US8984398B2 (en) * | 2008-08-28 | 2015-03-17 | Yahoo! Inc. | Generation of search result abstracts |
US20100131496A1 (en) * | 2008-11-26 | 2010-05-27 | Yahoo! Inc. | Predictive indexing for fast search |
US7949647B2 (en) | 2008-11-26 | 2011-05-24 | Yahoo! Inc. | Navigation assistance for search engines |
US8458171B2 (en) * | 2009-01-30 | 2013-06-04 | Google Inc. | Identifying query aspects |
US9330165B2 (en) * | 2009-02-13 | 2016-05-03 | Microsoft Technology Licensing, Llc | Context-aware query suggestion by mining log data |
US8041729B2 (en) * | 2009-02-20 | 2011-10-18 | Yahoo! Inc. | Categorizing queries and expanding keywords with a coreference graph |
KR101056412B1 (en) * | 2009-02-24 | 2011-08-11 | 전북대학교산학협력단 | Resampling System of Feedback Document Using Nested Cluster and Its Method |
US8447066B2 (en) | 2009-03-12 | 2013-05-21 | Google Inc. | Performing actions based on capturing information from rendered documents, such as documents under copyright |
WO2010105246A2 (en) | 2009-03-12 | 2010-09-16 | Exbiblio B.V. | Accessing resources based on capturing information from a rendered document |
US8392443B1 (en) * | 2009-03-17 | 2013-03-05 | Google Inc. | Refining search queries |
US8533202B2 (en) | 2009-07-07 | 2013-09-10 | Yahoo! Inc. | Entropy-based mixing and personalization |
US9436777B2 (en) * | 2009-08-13 | 2016-09-06 | Yahoo! Inc. | Method and system for causing a browser to preload web page components |
US8676828B1 (en) * | 2009-11-04 | 2014-03-18 | Google Inc. | Selecting and presenting content relevant to user input |
US9081799B2 (en) | 2009-12-04 | 2015-07-14 | Google Inc. | Using gestalt information to identify locations in printed information |
US9323784B2 (en) | 2009-12-09 | 2016-04-26 | Google Inc. | Image search using text-based elements within the contents of images |
US8738668B2 (en) | 2009-12-16 | 2014-05-27 | Renew Data Corp. | System and method for creating a de-duplicated data set |
US8875038B2 (en) | 2010-01-19 | 2014-10-28 | Collarity, Inc. | Anchoring for content synchronization |
US8498983B1 (en) * | 2010-01-29 | 2013-07-30 | Guangsheng Zhang | Assisting search with semantic context and automated search options |
US8176067B1 (en) * | 2010-02-24 | 2012-05-08 | A9.Com, Inc. | Fixed phrase detection for search |
US8560536B2 (en) * | 2010-03-11 | 2013-10-15 | Yahoo! Inc. | Methods, systems, and/or apparatuses for use in searching for information using computer platforms |
US20110258202A1 (en) * | 2010-04-15 | 2011-10-20 | Rajyashree Mukherjee | Concept extraction using title and emphasized text |
US8738635B2 (en) | 2010-06-01 | 2014-05-27 | Microsoft Corporation | Detection of junk in search result ranking |
US20110307504A1 (en) * | 2010-06-09 | 2011-12-15 | Microsoft Corporation | Combining attribute refinements and textual queries |
US8316019B1 (en) | 2010-06-23 | 2012-11-20 | Google Inc. | Personalized query suggestions from profile trees |
US8326861B1 (en) | 2010-06-23 | 2012-12-04 | Google Inc. | Personalized term importance evaluation in queries |
US20110320442A1 (en) * | 2010-06-25 | 2011-12-29 | International Business Machines Corporation | Systems and Methods for Semantics Based Domain Independent Faceted Navigation Over Documents |
US8600979B2 (en) | 2010-06-28 | 2013-12-03 | Yahoo! Inc. | Infinite browse |
US8694527B2 (en) * | 2010-06-30 | 2014-04-08 | International Business Machines Corporation | Simplified query generation from prior query results |
US8560562B2 (en) * | 2010-07-22 | 2013-10-15 | Google Inc. | Predictive query suggestion caching |
US8812733B1 (en) | 2010-08-19 | 2014-08-19 | Google Inc. | Transport protocol independent communications library |
DE212011100017U1 (en) * | 2010-08-19 | 2012-04-03 | David Black | Predictive query completion and predictive search results |
US9240020B2 (en) | 2010-08-24 | 2016-01-19 | Yahoo! Inc. | Method of recommending content via social signals |
US9779168B2 (en) | 2010-10-04 | 2017-10-03 | Excalibur Ip, Llc | Contextual quick-picks |
AU2010362878A1 (en) * | 2010-10-18 | 2013-05-02 | Pingar Holdings Limited | Universal search engine interface and application |
US20120095984A1 (en) * | 2010-10-18 | 2012-04-19 | Peter Michael Wren-Hilton | Universal Search Engine Interface and Application |
US8489604B1 (en) | 2010-10-26 | 2013-07-16 | Google Inc. | Automated resource selection process evaluation |
CN102646103B (en) * | 2011-02-18 | 2016-03-16 | 腾讯科技(深圳)有限公司 | The clustering method of term and device |
US8762356B1 (en) | 2011-07-15 | 2014-06-24 | Google Inc. | Detecting change in rate of input reception |
US8645825B1 (en) | 2011-08-31 | 2014-02-04 | Google Inc. | Providing autocomplete suggestions |
US9075799B1 (en) | 2011-10-24 | 2015-07-07 | NetBase Solutions, Inc. | Methods and apparatus for query formulation |
US9495462B2 (en) | 2012-01-27 | 2016-11-15 | Microsoft Technology Licensing, Llc | Re-ranking search results |
US8930181B2 (en) | 2012-12-06 | 2015-01-06 | Prashant Parikh | Automatic dynamic contextual data entry completion |
US10182054B2 (en) * | 2013-03-14 | 2019-01-15 | Open Text Sa Ulc | Systems, methods and computer program products for information integration across disparate information systems |
US10073956B2 (en) | 2013-03-14 | 2018-09-11 | Open Text Sa Ulc | Integration services systems, methods and computer program products for ECM-independent ETL tools |
US9898537B2 (en) | 2013-03-14 | 2018-02-20 | Open Text Sa Ulc | Systems, methods and computer program products for information management across disparate information systems |
US9501506B1 (en) | 2013-03-15 | 2016-11-22 | Google Inc. | Indexing system |
US9483568B1 (en) | 2013-06-05 | 2016-11-01 | Google Inc. | Indexing system |
US9449079B2 (en) * | 2013-06-28 | 2016-09-20 | Yandex Europe Ag | Method of and system for displaying a plurality of user-selectable refinements to a search query |
US9760620B2 (en) * | 2013-07-23 | 2017-09-12 | Salesforce.Com, Inc. | Confidently adding snippets of search results to clusters of objects |
US9846740B2 (en) * | 2013-09-09 | 2017-12-19 | Mimecast Services Ltd. | Associative search systems and methods |
US9536522B1 (en) * | 2013-12-30 | 2017-01-03 | Google Inc. | Training a natural language processing model with information retrieval model annotations |
CN103995870A (en) * | 2014-05-21 | 2014-08-20 | 百度在线网络技术(北京)有限公司 | Interactive searching method and device |
US10769176B2 (en) * | 2015-06-19 | 2020-09-08 | Richard Chino | Method and apparatus for creating and curating user collections for network search |
US9710468B2 (en) | 2014-09-04 | 2017-07-18 | Salesforce.Com, Inc. | Topic profile query creation |
CN104376115B (en) * | 2014-12-01 | 2017-08-29 | 北京奇虎科技有限公司 | A kind of fuzzy word based on global search determines method and device |
US10459608B2 (en) | 2014-12-01 | 2019-10-29 | Ebay Inc. | Mobile optimized shopping comparison |
KR102251811B1 (en) | 2015-01-02 | 2021-05-13 | 삼성전자주식회사 | Data storage device having internal hardware filter, and data processing system having the data storage device |
US10503764B2 (en) * | 2015-06-01 | 2019-12-10 | Oath Inc. | Location-awareness search assistance system and method |
US20170153798A1 (en) * | 2015-11-30 | 2017-06-01 | International Business Machines Corporation | Changing context and behavior of a ui component |
US10467291B2 (en) * | 2016-05-02 | 2019-11-05 | Oath Inc. | Method and system for providing query suggestions |
US10318563B2 (en) * | 2017-08-23 | 2019-06-11 | Lead Technologies, Inc. | Apparatus, method, and computer-readable medium for recognition of a digital document |
EP3635575A1 (en) | 2018-08-21 | 2020-04-15 | Google LLC. | Sibling search queries |
US11048767B2 (en) * | 2018-11-16 | 2021-06-29 | Sap Se | Combination content search |
Citations (43)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4965763A (en) * | 1987-03-03 | 1990-10-23 | International Business Machines Corporation | Computer method for automatic extraction of commonly specified information from business correspondence |
US5278980A (en) * | 1991-08-16 | 1994-01-11 | Xerox Corporation | Iterative technique for phrase query formation and an information retrieval system employing same |
US5675819A (en) * | 1994-06-16 | 1997-10-07 | Xerox Corporation | Document information retrieval using global word co-occurrence patterns |
US5771378A (en) * | 1993-11-22 | 1998-06-23 | Reed Elsevier, Inc. | Associative text search and retrieval system having a table indicating word position in phrases |
US5787421A (en) * | 1995-01-12 | 1998-07-28 | International Business Machines Corporation | System and method for information retrieval by using keywords associated with a given set of data elements and the frequency of each keyword as determined by the number of data elements attached to each keyword |
US5926811A (en) * | 1996-03-15 | 1999-07-20 | Lexis-Nexis | Statistical thesaurus, method of forming same, and use thereof in query expansion in automated text searching |
US5933822A (en) * | 1997-07-22 | 1999-08-03 | Microsoft Corporation | Apparatus and methods for an information retrieval system that employs natural language processing of search results to improve overall precision |
US5987457A (en) * | 1997-11-25 | 1999-11-16 | Acceleration Software International Corporation | Query refinement method for searching documents |
US6014665A (en) * | 1997-08-01 | 2000-01-11 | Culliss; Gary | Method for organizing information |
US6018733A (en) * | 1997-09-12 | 2000-01-25 | Infoseek Corporation | Methods for iteratively and interactively performing collection selection in full text searches |
US6044365A (en) * | 1993-09-01 | 2000-03-28 | Onkor, Ltd. | System for indexing and retrieving graphic and sound data |
US6067552A (en) * | 1995-08-21 | 2000-05-23 | Cnet, Inc. | User interface system and method for browsing a hypertext database |
US6128613A (en) * | 1997-06-26 | 2000-10-03 | The Chinese University Of Hong Kong | Method and apparatus for establishing topic word classes based on an entropy cost function to retrieve documents represented by the topic words |
US6154737A (en) * | 1996-05-29 | 2000-11-28 | Matsushita Electric Industrial Co., Ltd. | Document retrieval system |
US6182063B1 (en) * | 1995-07-07 | 2001-01-30 | Sun Microsystems, Inc. | Method and apparatus for cascaded indexing and retrieval |
US6266637B1 (en) * | 1998-09-11 | 2001-07-24 | International Business Machines Corporation | Phrase splicing and variable substitution using a trainable speech synthesizer |
US6295529B1 (en) * | 1998-12-24 | 2001-09-25 | Microsoft Corporation | Method and apparatus for indentifying clauses having predetermined characteristics indicative of usefulness in determining relationships between different texts |
US6324534B1 (en) * | 1999-09-10 | 2001-11-27 | Requisite Technology, Inc. | Sequential subset catalog search engine |
US6363378B1 (en) * | 1998-10-13 | 2002-03-26 | Oracle Corporation | Ranking of query feedback terms in an information retrieval system |
US20020052894A1 (en) * | 2000-08-18 | 2002-05-02 | Francois Bourdoncle | Searching tool and process for unified search using categories and keywords |
US6411950B1 (en) * | 1998-11-30 | 2002-06-25 | Compaq Information Technologies Group, Lp | Dynamic query expansion |
US20020083039A1 (en) * | 2000-05-18 | 2002-06-27 | Ferrari Adam J. | Hierarchical data-driven search and navigation system and method for information retrieval |
WO2002054289A1 (en) * | 2000-12-29 | 2002-07-11 | International Business Machines Corporation | Lossy index compression |
US6480843B2 (en) * | 1998-11-03 | 2002-11-12 | Nec Usa, Inc. | Supporting web-query expansion efficiently using multi-granularity indexing and query processing |
US20030004932A1 (en) * | 2001-06-20 | 2003-01-02 | International Business Machines Corporation | Method and system for knowledge repository exploration and visualization |
US6507841B2 (en) * | 1998-02-20 | 2003-01-14 | Hewlett-Packard Company | Methods of and apparatus for refining descriptors |
US6516312B1 (en) * | 2000-04-04 | 2003-02-04 | International Business Machine Corporation | System and method for dynamically associating keywords with domain-specific search engine queries |
US6546385B1 (en) * | 1999-08-13 | 2003-04-08 | International Business Machines Corporation | Method and apparatus for indexing and searching content in hardcopy documents |
US20030233618A1 (en) * | 2002-06-17 | 2003-12-18 | Canon Kabushiki Kaisha | Indexing and querying of structured documents |
US20040002962A1 (en) * | 2002-06-27 | 2004-01-01 | International Business Machines Corporation | Iconic representation of linked site characteristics |
US6678694B1 (en) * | 2000-11-08 | 2004-01-13 | Frank Meik | Indexed, extensible, interactive document retrieval system |
US20040044952A1 (en) * | 2000-10-17 | 2004-03-04 | Jason Jiang | Information retrieval system |
US20040139058A1 (en) * | 2002-12-30 | 2004-07-15 | Gosby Desiree D. G. | Document analysis and retrieval |
US20040158580A1 (en) * | 2001-12-19 | 2004-08-12 | David Carmel | Lossy index compression |
US20040205672A1 (en) * | 2000-12-29 | 2004-10-14 | International Business Machines Corporation | Automated spell analysis |
US20050043936A1 (en) * | 1999-06-18 | 2005-02-24 | Microsoft Corporation | System for improving the performance of information retrieval-type tasks by identifying the relations of constituents |
US6862710B1 (en) * | 1999-03-23 | 2005-03-01 | Insightful Corporation | Internet navigation using soft hyperlinks |
US6947930B2 (en) * | 2003-03-21 | 2005-09-20 | Overture Services, Inc. | Systems and methods for interactive search query refinement |
US6983239B1 (en) * | 2000-10-25 | 2006-01-03 | International Business Machines Corporation | Method and apparatus for embedding grammars in a natural language understanding (NLU) statistical parser |
US7028267B1 (en) * | 1999-12-07 | 2006-04-11 | Microsoft Corporation | Method and apparatus for capturing and rendering text annotations for non-modifiable electronic content |
US7092936B1 (en) * | 2001-08-22 | 2006-08-15 | Oracle International Corporation | System and method for search and recommendation based on usage mining |
US7236923B1 (en) * | 2002-08-07 | 2007-06-26 | Itt Manufacturing Enterprises, Inc. | Acronym extraction system and method of identifying acronyms and extracting corresponding expansions from text |
US7249121B1 (en) * | 2000-10-04 | 2007-07-24 | Google Inc. | Identification of semantic units from within a search query |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH08305710A (en) * | 1995-04-28 | 1996-11-22 | Toshiba Corp | Method for extracting key word of document and document retrieving device |
JP3607462B2 (en) * | 1997-07-02 | 2005-01-05 | 松下電器産業株式会社 | Related keyword automatic extraction device and document search system using the same |
JP2001005830A (en) * | 1999-06-23 | 2001-01-12 | Canon Inc | Information processor, its method and computer readable memory |
US7392238B1 (en) * | 2000-08-23 | 2008-06-24 | Intel Corporation | Method and apparatus for concept-based searching across a network |
JP3844193B2 (en) * | 2001-01-24 | 2006-11-08 | Kddi株式会社 | Information automatic filtering method, information automatic filtering system, and information automatic filtering program |
JP4888677B2 (en) * | 2001-07-06 | 2012-02-29 | 独立行政法人情報通信研究機構 | Document search system |
-
2003
- 2003-04-25 US US10/424,180 patent/US6947930B2/en not_active Expired - Lifetime
-
2004
- 2004-03-22 WO PCT/US2004/008713 patent/WO2004086192A2/en active Application Filing
- 2004-03-22 KR KR1020057017606A patent/KR100666064B1/en active IP Right Review Request
- 2004-03-22 JP JP2006507450A patent/JP5255766B2/en not_active Expired - Lifetime
- 2004-03-22 EP EP04758009A patent/EP1606704A4/en not_active Ceased
-
2005
- 2005-07-28 US US11/192,724 patent/US20060010126A1/en not_active Abandoned
-
2010
- 2010-08-05 JP JP2010176034A patent/JP5237335B2/en not_active Expired - Lifetime
-
2013
- 2013-02-21 JP JP2013031890A patent/JP5611390B2/en not_active Expired - Lifetime
-
2014
- 2014-04-21 JP JP2014087480A patent/JP5740029B2/en not_active Expired - Lifetime
Patent Citations (46)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4965763A (en) * | 1987-03-03 | 1990-10-23 | International Business Machines Corporation | Computer method for automatic extraction of commonly specified information from business correspondence |
US5278980A (en) * | 1991-08-16 | 1994-01-11 | Xerox Corporation | Iterative technique for phrase query formation and an information retrieval system employing same |
US6044365A (en) * | 1993-09-01 | 2000-03-28 | Onkor, Ltd. | System for indexing and retrieving graphic and sound data |
US5771378A (en) * | 1993-11-22 | 1998-06-23 | Reed Elsevier, Inc. | Associative text search and retrieval system having a table indicating word position in phrases |
US5675819A (en) * | 1994-06-16 | 1997-10-07 | Xerox Corporation | Document information retrieval using global word co-occurrence patterns |
US5787421A (en) * | 1995-01-12 | 1998-07-28 | International Business Machines Corporation | System and method for information retrieval by using keywords associated with a given set of data elements and the frequency of each keyword as determined by the number of data elements attached to each keyword |
US6282538B1 (en) * | 1995-07-07 | 2001-08-28 | Sun Microsystems, Inc. | Method and apparatus for generating query responses in a computer-based document retrieval system |
US6594658B2 (en) * | 1995-07-07 | 2003-07-15 | Sun Microsystems, Inc. | Method and apparatus for generating query responses in a computer-based document retrieval system |
US6182063B1 (en) * | 1995-07-07 | 2001-01-30 | Sun Microsystems, Inc. | Method and apparatus for cascaded indexing and retrieval |
US6067552A (en) * | 1995-08-21 | 2000-05-23 | Cnet, Inc. | User interface system and method for browsing a hypertext database |
US5926811A (en) * | 1996-03-15 | 1999-07-20 | Lexis-Nexis | Statistical thesaurus, method of forming same, and use thereof in query expansion in automated text searching |
US6154737A (en) * | 1996-05-29 | 2000-11-28 | Matsushita Electric Industrial Co., Ltd. | Document retrieval system |
US6128613A (en) * | 1997-06-26 | 2000-10-03 | The Chinese University Of Hong Kong | Method and apparatus for establishing topic word classes based on an entropy cost function to retrieve documents represented by the topic words |
US5933822A (en) * | 1997-07-22 | 1999-08-03 | Microsoft Corporation | Apparatus and methods for an information retrieval system that employs natural language processing of search results to improve overall precision |
US6014665A (en) * | 1997-08-01 | 2000-01-11 | Culliss; Gary | Method for organizing information |
US6018733A (en) * | 1997-09-12 | 2000-01-25 | Infoseek Corporation | Methods for iteratively and interactively performing collection selection in full text searches |
US5987457A (en) * | 1997-11-25 | 1999-11-16 | Acceleration Software International Corporation | Query refinement method for searching documents |
US6507841B2 (en) * | 1998-02-20 | 2003-01-14 | Hewlett-Packard Company | Methods of and apparatus for refining descriptors |
US6266637B1 (en) * | 1998-09-11 | 2001-07-24 | International Business Machines Corporation | Phrase splicing and variable substitution using a trainable speech synthesizer |
US6363378B1 (en) * | 1998-10-13 | 2002-03-26 | Oracle Corporation | Ranking of query feedback terms in an information retrieval system |
US6480843B2 (en) * | 1998-11-03 | 2002-11-12 | Nec Usa, Inc. | Supporting web-query expansion efficiently using multi-granularity indexing and query processing |
US6411950B1 (en) * | 1998-11-30 | 2002-06-25 | Compaq Information Technologies Group, Lp | Dynamic query expansion |
US6295529B1 (en) * | 1998-12-24 | 2001-09-25 | Microsoft Corporation | Method and apparatus for indentifying clauses having predetermined characteristics indicative of usefulness in determining relationships between different texts |
US6862710B1 (en) * | 1999-03-23 | 2005-03-01 | Insightful Corporation | Internet navigation using soft hyperlinks |
US20050043936A1 (en) * | 1999-06-18 | 2005-02-24 | Microsoft Corporation | System for improving the performance of information retrieval-type tasks by identifying the relations of constituents |
US6546385B1 (en) * | 1999-08-13 | 2003-04-08 | International Business Machines Corporation | Method and apparatus for indexing and searching content in hardcopy documents |
US6324534B1 (en) * | 1999-09-10 | 2001-11-27 | Requisite Technology, Inc. | Sequential subset catalog search engine |
US7028267B1 (en) * | 1999-12-07 | 2006-04-11 | Microsoft Corporation | Method and apparatus for capturing and rendering text annotations for non-modifiable electronic content |
US6516312B1 (en) * | 2000-04-04 | 2003-02-04 | International Business Machine Corporation | System and method for dynamically associating keywords with domain-specific search engine queries |
US20020083039A1 (en) * | 2000-05-18 | 2002-06-27 | Ferrari Adam J. | Hierarchical data-driven search and navigation system and method for information retrieval |
US20020052894A1 (en) * | 2000-08-18 | 2002-05-02 | Francois Bourdoncle | Searching tool and process for unified search using categories and keywords |
US7249121B1 (en) * | 2000-10-04 | 2007-07-24 | Google Inc. | Identification of semantic units from within a search query |
US20040044952A1 (en) * | 2000-10-17 | 2004-03-04 | Jason Jiang | Information retrieval system |
US6983239B1 (en) * | 2000-10-25 | 2006-01-03 | International Business Machines Corporation | Method and apparatus for embedding grammars in a natural language understanding (NLU) statistical parser |
US6678694B1 (en) * | 2000-11-08 | 2004-01-13 | Frank Meik | Indexed, extensible, interactive document retrieval system |
US20040205672A1 (en) * | 2000-12-29 | 2004-10-14 | International Business Machines Corporation | Automated spell analysis |
WO2002054289A1 (en) * | 2000-12-29 | 2002-07-11 | International Business Machines Corporation | Lossy index compression |
US6725217B2 (en) * | 2001-06-20 | 2004-04-20 | International Business Machines Corporation | Method and system for knowledge repository exploration and visualization |
US20030004932A1 (en) * | 2001-06-20 | 2003-01-02 | International Business Machines Corporation | Method and system for knowledge repository exploration and visualization |
US7092936B1 (en) * | 2001-08-22 | 2006-08-15 | Oracle International Corporation | System and method for search and recommendation based on usage mining |
US20040158580A1 (en) * | 2001-12-19 | 2004-08-12 | David Carmel | Lossy index compression |
US20030233618A1 (en) * | 2002-06-17 | 2003-12-18 | Canon Kabushiki Kaisha | Indexing and querying of structured documents |
US20040002962A1 (en) * | 2002-06-27 | 2004-01-01 | International Business Machines Corporation | Iconic representation of linked site characteristics |
US7236923B1 (en) * | 2002-08-07 | 2007-06-26 | Itt Manufacturing Enterprises, Inc. | Acronym extraction system and method of identifying acronyms and extracting corresponding expansions from text |
US20040139058A1 (en) * | 2002-12-30 | 2004-07-15 | Gosby Desiree D. G. | Document analysis and retrieval |
US6947930B2 (en) * | 2003-03-21 | 2005-09-20 | Overture Services, Inc. | Systems and methods for interactive search query refinement |
Cited By (75)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8117139B2 (en) | 2003-04-04 | 2012-02-14 | Icosystem Corporation | Methods and systems for interactive evolutionary computing (IEC) |
US8117140B2 (en) | 2003-08-01 | 2012-02-14 | Icosystem Corporation | Methods and systems for applying genetic operators to determine systems conditions |
US7519608B2 (en) * | 2004-03-22 | 2009-04-14 | Sliccware | Secure virtual data warehousing system and method |
US20050210049A1 (en) * | 2004-03-22 | 2005-09-22 | Sliccware | Secure virtual data warehousing system and method |
US20060010117A1 (en) * | 2004-07-06 | 2006-01-12 | Icosystem Corporation | Methods and systems for interactive search |
US20100211558A1 (en) * | 2004-07-06 | 2010-08-19 | Icosystem Corporation | Methods and apparatus for interactive searching techniques |
US20060161520A1 (en) * | 2005-01-14 | 2006-07-20 | Microsoft Corporation | System and method for generating alternative search terms |
US8209314B2 (en) * | 2005-04-19 | 2012-06-26 | International Business Machines Corporation | Search criteria control system and method |
US20090106241A1 (en) * | 2005-04-19 | 2009-04-23 | International Business Machines Corporation | Search criteria control system and method |
US20070033178A1 (en) * | 2005-06-23 | 2007-02-08 | Cognos Incorporated | Quality of service feedback for technology-neutral data reporting |
US7844601B2 (en) * | 2005-06-23 | 2010-11-30 | International Business Machines Corporation | Quality of service feedback for technology-neutral data reporting |
US20070027848A1 (en) * | 2005-07-29 | 2007-02-01 | Microsoft Corporation | Smart search for accessing options |
US20070027852A1 (en) * | 2005-07-29 | 2007-02-01 | Microsoft Corporation | Smart search for accessing options |
US20070067212A1 (en) * | 2005-09-21 | 2007-03-22 | Eric Bonabeau | System and method for aiding product design and quantifying acceptance |
US8423323B2 (en) | 2005-09-21 | 2013-04-16 | Icosystem Corporation | System and method for aiding product design and quantifying acceptance |
US20070208738A1 (en) * | 2006-03-03 | 2007-09-06 | Morgan Brian S | Techniques for providing suggestions for creating a search query |
US7676460B2 (en) * | 2006-03-03 | 2010-03-09 | International Business Machines Corporation | Techniques for providing suggestions for creating a search query |
US20070271255A1 (en) * | 2006-05-17 | 2007-11-22 | Nicky Pappo | Reverse search-engine |
US20070288451A1 (en) * | 2006-06-07 | 2007-12-13 | Marek Meyer | Generating searchable keywords |
US7849078B2 (en) * | 2006-06-07 | 2010-12-07 | Sap Ag | Generating searchable keywords |
US8380733B2 (en) * | 2006-07-18 | 2013-02-19 | Cisco Technology, Inc. | Methods and apparatuses for dynamically displaying search suggestions |
US20110040778A1 (en) * | 2006-07-18 | 2011-02-17 | Webex Communications, Inc. | Methods and apparatuses for dynamically displaying search suggestions |
US20080134033A1 (en) * | 2006-11-30 | 2008-06-05 | Microsoft Corporation | Rank graph |
US7793230B2 (en) | 2006-11-30 | 2010-09-07 | Microsoft Corporation | Search term location graph |
US20080281857A1 (en) * | 2007-05-10 | 2008-11-13 | Xerox Corporation | Event hierarchies and memory organization for structured data retrieval |
US7831587B2 (en) * | 2007-05-10 | 2010-11-09 | Xerox Corporation | Event hierarchies and memory organization for structured data retrieval |
US8515935B1 (en) | 2007-05-31 | 2013-08-20 | Google Inc. | Identifying related queries |
US8732153B1 (en) | 2007-05-31 | 2014-05-20 | Google Inc. | Identifying related queries |
US20090019034A1 (en) * | 2007-06-26 | 2009-01-15 | Seeqpod, Inc. | Media discovery and playlist generation |
US8117185B2 (en) * | 2007-06-26 | 2012-02-14 | Intertrust Technologies Corporation | Media discovery and playlist generation |
US8527506B2 (en) | 2007-06-26 | 2013-09-03 | Intertrust Technologies Corporation | Media discovery and playlist generation |
US9846744B2 (en) | 2007-06-26 | 2017-12-19 | Intertrust Technologies Corporation | Media discovery and playlist generation |
US8271493B2 (en) * | 2007-10-11 | 2012-09-18 | Oracle International Corporation | Extensible mechanism for grouping search results |
US20090100039A1 (en) * | 2007-10-11 | 2009-04-16 | Oracle International Corp | Extensible mechanism for grouping search results |
US8301651B2 (en) * | 2007-11-21 | 2012-10-30 | Chacha Search, Inc. | Method and system for improving utilization of human searchers |
US20090132500A1 (en) * | 2007-11-21 | 2009-05-21 | Chacha Search, Inc. | Method and system for improving utilization of human searchers |
US9064025B2 (en) | 2007-11-21 | 2015-06-23 | Chacha Search, Inc. | Method and system for improving utilization of human searchers |
US9122743B2 (en) * | 2008-01-30 | 2015-09-01 | International Business Machines Corporation | Enhanced search query modification |
US20090192985A1 (en) * | 2008-01-30 | 2009-07-30 | International Business Machines Corporation | Method, system, and program product for enhanced search query modification |
US9183323B1 (en) | 2008-06-27 | 2015-11-10 | Google Inc. | Suggesting alternative query phrases in query results |
US8171043B2 (en) * | 2008-10-24 | 2012-05-01 | Yahoo! Inc. | Methods for improving the diversity of image search results |
US20100114933A1 (en) * | 2008-10-24 | 2010-05-06 | Vanessa Murdock | Methods for improving the diversity of image search results |
US10210179B2 (en) * | 2008-11-18 | 2019-02-19 | Excalibur Ip, Llc | Dynamic feature weighting |
US20100125568A1 (en) * | 2008-11-18 | 2010-05-20 | Van Zwol Roelof | Dynamic feature weighting |
US9740780B1 (en) | 2009-03-23 | 2017-08-22 | Google Inc. | Autocompletion using previously submitted query data |
US8930350B1 (en) | 2009-03-23 | 2015-01-06 | Google Inc. | Autocompletion using previously submitted query data |
US9652537B2 (en) | 2009-05-22 | 2017-05-16 | Microsoft Technology Licensing, Llc | Identifying terms associated with queries |
US8190601B2 (en) * | 2009-05-22 | 2012-05-29 | Microsoft Corporation | Identifying task groups for organizing search results |
US8572074B2 (en) | 2009-05-22 | 2013-10-29 | Microsoft Corporation | Identifying task groups for organizing search results |
US20100299343A1 (en) * | 2009-05-22 | 2010-11-25 | Microsoft Corporation | Identifying Task Groups for Organizing Search Results |
US9378247B1 (en) | 2009-06-05 | 2016-06-28 | Google Inc. | Generating query refinements from user preference data |
US8918417B1 (en) * | 2009-06-05 | 2014-12-23 | Google Inc. | Generating query refinements from user preference data |
US9092528B1 (en) | 2009-08-28 | 2015-07-28 | Google Inc. | Providing result-based query suggestions |
US9563692B1 (en) | 2009-08-28 | 2017-02-07 | Google Inc. | Providing result-based query suggestions |
US10459989B1 (en) | 2009-08-28 | 2019-10-29 | Google Llc | Providing result-based query suggestions |
US8682900B2 (en) | 2009-12-08 | 2014-03-25 | International Business Machines Corporation | System, method and computer program product for documents retrieval |
US20110137912A1 (en) * | 2009-12-08 | 2011-06-09 | International Business Machines Corporation | System, method and computer program product for documents retrieval |
US9110993B1 (en) | 2010-01-15 | 2015-08-18 | Google Inc. | Search query reformulation using result term occurrence count |
US8849785B1 (en) | 2010-01-15 | 2014-09-30 | Google Inc. | Search query reformulation using result term occurrence count |
US20120110453A1 (en) * | 2010-10-29 | 2012-05-03 | Microsoft Corporation | Display of Image Search Results |
US9251185B2 (en) | 2010-12-15 | 2016-02-02 | Girish Kumar | Classifying results of search queries |
CN102567475A (en) * | 2010-12-15 | 2012-07-11 | 微软公司 | User interface for interactive query reformulation |
US20120158765A1 (en) * | 2010-12-15 | 2012-06-21 | Microsoft Corporation | User Interface for Interactive Query Reformulation |
US10068022B2 (en) * | 2011-06-03 | 2018-09-04 | Google Llc | Identifying topical entities |
US20150278366A1 (en) * | 2011-06-03 | 2015-10-01 | Google Inc. | Identifying topical entities |
WO2013016288A1 (en) * | 2011-07-27 | 2013-01-31 | Microsoft Corporation | Utilization of features extracted from structured documents to improve search relevance |
US8788436B2 (en) | 2011-07-27 | 2014-07-22 | Microsoft Corporation | Utilization of features extracted from structured documents to improve search relevance |
US8566340B2 (en) * | 2011-12-07 | 2013-10-22 | Microsoft Corporation | Provision of query suggestions independent of query logs |
US9767144B2 (en) | 2012-04-20 | 2017-09-19 | Microsoft Technology Licensing, Llc | Search system with query refinement |
US10108699B2 (en) | 2013-01-22 | 2018-10-23 | Microsoft Technology Licensing, Llc | Adaptive query suggestion |
US20170270159A1 (en) * | 2013-03-14 | 2017-09-21 | Google Inc. | Determining query results in response to natural language queries |
US20230032882A1 (en) * | 2015-09-04 | 2023-02-02 | Palantir Technologies Inc. | Systems and methods for database investigation tool |
US11360958B2 (en) | 2017-09-29 | 2022-06-14 | Apple Inc. | Techniques for indexing and querying a set of documents at a computing device |
WO2022231646A1 (en) * | 2021-04-30 | 2022-11-03 | CS Disco, Inc. | Systems and methods for searching related documents and associated search operators |
US11790017B2 (en) | 2021-04-30 | 2023-10-17 | CS Disco, Inc. | Systems and methods for searching related documents and associated search operators |
Also Published As
Publication number | Publication date |
---|---|
JP2010257488A (en) | 2010-11-11 |
US20040186827A1 (en) | 2004-09-23 |
EP1606704A2 (en) | 2005-12-21 |
KR100666064B1 (en) | 2007-01-10 |
JP5740029B2 (en) | 2015-06-24 |
KR20060002831A (en) | 2006-01-09 |
WO2004086192A2 (en) | 2004-10-07 |
JP2013109781A (en) | 2013-06-06 |
JP2014160498A (en) | 2014-09-04 |
JP5237335B2 (en) | 2013-07-17 |
JP5255766B2 (en) | 2013-08-07 |
JP2006523344A (en) | 2006-10-12 |
WO2004086192A3 (en) | 2005-02-17 |
EP1606704A4 (en) | 2006-07-26 |
JP5611390B2 (en) | 2014-10-22 |
US6947930B2 (en) | 2005-09-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US6947930B2 (en) | Systems and methods for interactive search query refinement | |
US5963965A (en) | Text processing and retrieval system and method | |
Kowalski et al. | Information storage and retrieval systems: theory and implementation | |
US7676452B2 (en) | Method and apparatus for search optimization based on generation of context focused queries | |
CA2536265C (en) | System and method for processing a query | |
US6957213B1 (en) | Method of utilizing implicit references to answer a query | |
US6286000B1 (en) | Light weight document matcher | |
US7076484B2 (en) | Automated research engine | |
US20020073079A1 (en) | Method and apparatus for searching a database and providing relevance feedback | |
US20050071332A1 (en) | Search query processing to identify related search terms and to correct misspellings of search terms | |
US20030200198A1 (en) | Method and system for performing phrase/word clustering and cluster merging | |
US20070192293A1 (en) | Method for presenting search results | |
US20020174095A1 (en) | Very-large-scale automatic categorizer for web content | |
WO2009117835A1 (en) | Search system and method for serendipitous discoveries with faceted full-text classification | |
WO2005124599A2 (en) | Content search in complex language, such as japanese | |
US7024405B2 (en) | Method and apparatus for improved internet searching | |
US20050114317A1 (en) | Ordering of web search results | |
Kantorski et al. | Automatic filling of hidden web forms: a survey | |
EP1605371A1 (en) | Content search in complex language, such as japanese | |
KR100434718B1 (en) | Method and system for indexing document | |
Zhang | Search term selection and document clustering for query suggestion | |
Lin | Intelligent Internet Information Systems in Knowledge Acquisition: Techniques and Applications | |
Eskicioğlu | A Search Engine for Turkish with Stemming |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: OVERTURE SERVICES, INC, CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ALTA VISTA COMPANY;REEL/FRAME:021951/0057 Effective date: 20030918 |
|
AS | Assignment |
Owner name: YAHOO| INC, CALIFORNIA Free format text: MERGER;ASSIGNOR:OVERTURE SERVICES, INC;REEL/FRAME:021968/0115 Effective date: 20081001 |
|
AS | Assignment |
Owner name: ALTA VISTA COMPANY, CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ANICK, PETER G.;GOURLAY, ALASTAIR;THRALL, JOHN;SIGNING DATES FROM 20020418 TO 20030417;REEL/FRAME:033859/0308 |
|
AS | Assignment |
Owner name: JOLLIFY MANAGEMENT LIMITED, VIRGIN ISLANDS, BRITIS Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:YAHOO| INC.;REEL/FRAME:034670/0250 Effective date: 20141202 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |