WO1995002221A1 - Organisation et consultation d'une base de donnees casuelle - Google Patents
Organisation et consultation d'une base de donnees casuelle Download PDFInfo
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- WO1995002221A1 WO1995002221A1 PCT/US1994/007569 US9407569W WO9502221A1 WO 1995002221 A1 WO1995002221 A1 WO 1995002221A1 US 9407569 W US9407569 W US 9407569W WO 9502221 A1 WO9502221 A1 WO 9502221A1
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- 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
- G06F16/3329—Natural language query formulation or dialogue systems
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- 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
- G06F16/35—Clustering; Classification
Definitions
- This invention relates to case-based organizing and querying of a database.
- Prior art methods of retrieving information generally require preparation of a query, in which objects to be searched for are described in some formal manner. This imposes additional effort on the searcher, and generally also requires that the searcher be familiar with the subject matter to be searched, with the organization and indexing of the database, and with a formal query language. Accordingly, it would be advantageous for the searcher to be able to describe the query in a natural and relatively informal or unstructured manner, such as a description in a natural language.
- the response may be organized by quality of match. In another aspect, the response may be organized into clusters of related objects.
- the invention provides a system for case-based organizing and querying of a database.
- the database may comprise a set of objects, such as a set of documents including text.
- the database may be organized by examining each object and associating that object with a set of property values, such as (in the case of text documents) a set of keywords or other indicators of content.
- a document may be associated with those words which appear more frequently in the document than in the database at large, or which appear in early text of the document, or which appear in a title.
- the system may be responsive to a query by associating the query with a similar set of property values and performing case-based matching or other fuzzy associative matching on the objects of the database for objects which are similar.
- the query may be natural-language text and may be associated with keywords or other indicators of its content.
- the system may present matched objects in response to the query, may respond to iterative refinement of the query (in similar manner to iterative case-based methods shown in those co-pending applications which have been incorporated by reference) , and may order matched objects by quality of match.
- the system may also examine the collection of matched objects and organize them for presentation ; for example, the system may group matched objects into clusters of objects which have similar properties, which relate to similar content, or which have similar likelihood to be of relevance to the query or of interest to an operator posing the query.
- the system may respond to the result of organizing matched objects for presentation with suggestions for iterative refinement of the query.
- the system may therefore be capable of producing improved recall and precision over prior art techniques.
- Figure 1 shows a block diagram of a database explorer and filter system.
- Figure 2 shows a data flow diagram of a method of filtering documents.
- Figure 3 shows a data flow diagram of a method of processing queries.
- Figure 4 shows a data flow diagram of a method of processing hit tables.
- Figure 5 shows a process flow diagram of a method of clustering hit tables.
- Figure 6 shows an example explorer user interface screen as viewed by an operator.
- Figure 7 shows a second example explorer user interface screen, as viewed by an operator, in which clusters are displayed.
- Figure 8 shows an example explorer user interface screen, as viewed by an operator, in which settings may be set by the operator.
- Appendix A shows a table of parts of speech and a set of lexical rules for the English language, which may be used for the tag-and-segment-text process or the tag-and-segment-text process in a preferred embodiment.
- Appendix B shows an output of a test run of an example filter when applied to a portion of an example multimedia encyclopedia used as a database, available as "Microsoft Encarta” from Microsoft Corporation of Redmond, Washington.
- the invention may operate in conjunction with a computing system, including a processor and a memory, generally configured as is well known in the art; the memory may include primary memory for stored programs and for data and secondary memory for extensive storage of large numbers of objects.
- the memory may comprise a sizable database of objects, as is well known in the art of databases, and such objects may comprise various types of computing and data-storage structures.
- the database may be a relational database, an unstructured collection of objects, or some other database format.
- Such other types of objects may include source code, object code, binary values, numeric values, text or other symbolic values, representations of sound and/or picture signals or other signals, multimedia, data structures for rule-based or case-based systems, artificial neural networks, linked data structures such as linked lists, mathematical structures such as equations, polynomials, matrices or tensors, and other data types known in at least one of the many fields of computing.
- Figure 1 shows a block diagram of a database explorer and filter system.
- a system 101 for case-based organizing and querying of a database 102 may comprise a filter 103, for organizing the database 102 so as to be responsive to a query 104, an explorer 105, for selecting a set of objects 106 in the database 102 which are responsive to that query 104, and an object file system 107, for accessing the database 102.
- the database 102 may generally be of a type which is known in the art, such as a collection of text objects supported by Cairo Milestone 4 running under the Windows NT system version 297, available from Microsoft Corporation of Redmond, Washington, and may be accessed in conjunction with the object file system 107 of that product.
- the filter 103 may operate at an initialization time, such as when the processor is first started or before the first query 104 is presented to the explorer 105.
- the filter 103 may also operate in an incremental mode, e.g., by updating its organization of the database 102 periodically, such as upon the passage of a fixed period of time, when a fixed number of objects 106 are changed or added to the database 102, when the operation of the explorer 105 is degraded below some predetermined level, when triggered by an operator 108 in conjunction with a user interface 109 (e.g., when a query is presented, by a specific command to do so, or as a side effect of another operation) , or otherwise as determined by the database 102 or an external manager.
- the filter 103 may examine each of the objects 106 (or some predetermined subset of objects 106) in the database 102 and associate each object 106 it examines (or some predetermined subset of those objects 106) with a set of properties.
- those properties may be keywords or phrases which are found in the object 106, but may also comprise other property values, such as the language the text is written in, the length of the text, or the reading level or other measure associated with the text (including measures of complexity, detail, redundancy, writing style, "fog", or other known measures of text, e.g., known in the art of grammar checking and correction) .
- the objects 106 with their properties may be treated as a set of cases to be matched by a CBR engine 110 (operating with the object file system 107) with a test case generated from the query 104.
- Each case may generally comprise an object 106 plus the properties that object 106 was associated with, e.g., key words and phrases found in that object.
- these properties may include a lexicon of words and noun phrases found in the object 106, including at least some of these words labelled as a set of "header words" or "relevant words” .
- the explorer 105 may generally operate at a question time, such as when one or more queries 104 is presented to the explorer 105.
- the ej ⁇ lorer 105 may be invoked by the operator 108 in conjunction with the user interface 109, which user interface 109 may allow the operator to trigger operation of the explorer 105 and to present one or more queries 104 to the explorer 105.
- the user interface 109 may be one such as the user interface presented by the Windows NT system referred to herein.
- the operator 108 may be a human being, but those of ordinary skill with recognize, after perusal of the application, that the operator 108 may comprise a network connection, an external management program, or an Al program.
- the explorer 105 may generate a response 111 including a set of matching cases (i.e., objects 106 with their properties) , which may be presented to the operator 108 by means of the user interface 109, such as the user interface presented by the Windows NT system referred to herein. I augmented by features described herein.
- the filter 103 and the explorer 105 may operate in conjunction with the object file system 107 (and in particular the CBR engine 110 thereof) , which may respond to a set of properties formed into a vector query 112 directed at the database 102, and may return a hit table 113 of those objects 106 in the database 102 which have the indicated properties.
- the CBR engine 110 may use case-based matching and other techniques such as those shown in those co- pending applications which have been incorporated by reference.
- Figure 2 shows a data flow diagram of a method of filtering documents.
- a document 201 (an object 106 which comprises text, such as a pure text document or a text document formatted for a word-processing program) may be input to the filter 103 for examination.
- the filter 103 may process the text by a tag-and-segment-text process 202, which may lexically analyze the document 201, e.g., by means of a known lexical analysis technique.
- the tag-and-segment-text process 202 may extract a set of single terms 203 and generate a set of header words 204 found in the document 201.
- the header words 204 may comprise those words which occur in an initial part of the object 106, or in a title, subject line, topical paragraph, or abstract.
- the header words 204 may comprise the first three things mentioned in the document 201.
- the tag-and-segment-text process 202 may also tag words in the document 201 with their parts of speech and parse them into a set of sentences 205.
- the sentences 205 may be input to an extract-noun-phrases process 206, which may further lexically analyze the document 201, e.g., by means of a known lexical analysis technique, to extract a set of noun phrases 207 and generate a lexicon 208 thereof.
- the tag-and-segment-text process 202 may use a grammar of the English language, but other natural languages, and even formal specification languages such as programming languages, would also be suitable.
- the tag-and-segment-text process 202 may also recognize and generate a set of proper nouns 209.
- the set of proper nouns 209 may be determined by known rules, e.g., that proper nouns generally comprise strings of words each starting with an upper-case letter, or by reference to a dictionary of known proper names.
- the set of proper nouns 209 may be input, along with at least some of the single terms 203, to a determine-relevant-words process 210, which may extract a set of relevant words 211.
- the set of relevant words 211 may be determined with reference to the frequency of those words in the object 106 (with respect to the entire text found in the object 106) and with reference to the frequency of those words in the database 102, with respect to the text corpus of the database 102.
- the ratio for each word (frequency in the object 106) divided by (frequency in the database 102) may be computed, and the set of relevant words 211 may comprise those words whose relative frequency exceeds a threshold, e.g., a predetermined threshold such as a 1:1 ratio.
- the filter 103 is described herein for a specific set of properties of the text which may be extracted. However, it would be clear to those of ordinary skill, after perusal of this application, that extraction of other properties could be readily accomplished, and is within the scope and spirit of the invention. Such other properties could include the language the text is written in (or for English-language text, the number of foreign words used) , the length of the text, or the reading level or other measure associated with the text (including measures of complexity, detail, redundancy, writing style, "fog", or other known measures of text, e.g., known in the art of grammar checking and correction) .
- the extract-noun-phrases process 206 and the determine-relevant-words process 211 may proceed in parallel, e.g., by execution on multiple processors or by multiple tasks or threads in a multitasking or multithreaded environment.
- the filter 103 may mark each object 106 with the properties it determines (or alternatively may create a separate object 106 relating each documentary object 106 to its properties) , so that the object 106 and its properties may be treated as a case in a case-base.
- the set of cases may be matched to a test case by a CBR engine 110, using techniques like those described in copending applications (1) Serial No. 07/ 664,561, filed March 4, 1991 in the name of inventors Bradley P. Allen and S. Daniel Lee, titled “CASE-BASED REASONING SYSTEM”; (2) Serial No. 07/ 869,935, filed April 15, 1992 in the name of inventor Bradley P.
- Figure 3 shows a data flow diagram of a method of processing queries.
- the query 104 entered in free text by the operator 108, may be input to the explorer 105 for examination.
- the explorer 105 may process the text by a tag- and-segment-text process 301, which may lexically analyze the document 201, e.g., by means of a known lexical analysis technique, similarly to the tag-and-segment-text process 202 of the filter 103.
- the tag-and-segment-text process 301 may extract a set of single terms 302, similarly to the tag-and-segment-text process 202 and the set of single terms 203 of the filter 103.
- the tag-and-segment-text process 301 may also tag words in the document 201 with their parts of speech and parse them into a set of sentences 303, similarly to the tag-and-segment- text process 202 and the sentences 205 of the filter 103.
- the sentences 303 may be input to an extract-noun-phrases process 304, which may further lexically analyze the document 201, e.g., by means of a known lexical analysis technique, to extract a set of noun phrases 305, similarly to the extract-noun-phrases process 206 and the noun phrases 207 of the filter 103.
- the tag-and-segment-text process 301 may also recognize and generate a set of proper nouns 306, similarly to the tag-and- segment-text process 202 and the proper nouns 209 of the filter 103.
- the noun phrases 305, single terms 302, and proper nouns 306, a rank threshold 307, and a set of selected subtopics 308 (subtopics selected by the operator 108 to refine the query 104) may be input to a generate-query process 309, which may generate a set of query terms 310 and a query parse tree 311.
- the tag-and-segment-text process 301, the extract-noun-phrases process 304, and the generate-query process 309 may proceed as asynchronously as possible, e.g., by execution on multiple processors or by multiple tasks or threads in a multitasking or multithreaded environment.
- the query terms 310 and the query parse tree 311 may be input to the CBR engine 110 in the object file system 107, and may perform case-based matching or other fuzzy associative matching on the objects 106 in the database 102 for objects which are similar to the query 104, as described by the query terms 310 and the query parse tree 311, and which have a match quality at least as good as the rank threshold 307. (As noted with regard to the user interface 109, the selected subtopics 308 are added to the text of the query 104.)
- the object file system 107 may generate the hit table 113 of matched objects 106.
- Figure 4 shows a data flow diagram of a method of processing hit tables.
- the hit table 113 and the relevant words 211 may be input to a cluster hits process 401, which (if clustering is enabled) collects the matched objects 106 into clusters, and may output a set of clusters 402 in response.
- Each cluster 402 may comprise a set of objects 106, selected for collective closeness with regard to all objects 106 in the hit table 113.
- the cluster hits process 401 is further described with regard to figure 5.
- the hit table 113, the relevant words 211, and the lexicon 208 may be input to a first generate-topics (from relevant words) process 403, while the lexicon 208 and the query terms 310 may be input to a second generate-topics (from query words) process 403. Together the two generate-topics processes 403 may output a set of topics 404 and subtopics 405.
- the generate-topics process 403 may examine the lexicon 208 of noun phrases 207 with a rule- based inference engine (not shown) .
- a rule- based inference engine is the ART-IM system, available from Inference Corporation in El Segundo, California.
- the inference engine may detect particular patterns in the noun phrases 207 which indicate semantic relations between the words in those noun phrases 207. For example, the noun phrase
- the generate-topics process 403 may thus construct a phrase lattice., showing each noun phrase 207 as being inclusive of (above) , included in (below) , or incommensurate with (neither above nor below) each other noun phrase 207.
- the generate-topics (from relevant words) process 403 may restrict the phrase lattice to those noun phrases 207 which include relevant words 211 of the objects 106 in the hit table 113.
- the second generate-topics (from query words) process 403 may operate in similar manner as the first generate-topics (from relevant words) process 403 and may restrict the phrase lattice to those noun phrases 305 which include relevant words 211 of the query.
- Figure 5 shows a process flow diagram of a method of clustering hit tables.
- the cluster hits process 401 may operate by means of a genetic algorithm, in which an initial configuration and a set of genetic operators are specified, and the set of solutions is formed by simulation of random "evolution" of a population of possible solutions, using the method of steady-state reproduction without duplicates.
- Genetic algorithms are well known in the art, and are described in further detail in "Foundations of Genetic Algorithms", ed. Gregory J.E. Rawlins (Morgan Kaufmann Publishers: San Mateo, California 1991). It would be clear to those of ordinary skill in the art that the parameters of the genetic algorithm, and even the type of genetic algorithm performed could be varied substantially and still remain within the scope and spirit of the invention.
- a number of clusters 402 is selected.
- the number of clusters 402 may vary from a known minimum to a known maximum, settable by the operator 108.
- the genetic algorithm of the following steps is repeated for each permissible number of clusters 402, and the best solution adopted.
- an initiate-clusters step 502 a set of possible clusters 402 is selected; this is a single "gene”. A random population of genes is selected-. Each cluster 402 is represented by the centroid of the objects 106 which would comprise that cluster 402. Thus, when a solution of clusters 402 is selected, each object 106 is assigned to the cluster 402 which it best matches.
- the genetic algorithm of the following steps is repeated for a known period of time, settable by the operator 108.
- the best available solution i.e., the gene with the best quality
- Each object 106 is assigned to the cluster 402 to which it is the closest.
- all genes in the population are evaluated for quality, and the gene with the least quality is removed.
- the statistical measure "category utility" is computed; i.e., the utility of each cluster 402 in distinguishing between an object 106 in one cluster 402 from an object in another cluster 402.
- matching for clusters 402 is performed using relevant words 211, it would be clear to those of ordinary skill, after perusal of this application, that other properties of the objects 106 could be used as well, such as the read/write date of the object 106, and that doing so would be within the scope and spirit of the invention.
- a genetic-operator step 504 one of three operators is selected and employed to create a new gene: (1) Mutation-1. The new gene is randomly created. (2) Mutation-2. An existing gene is copied, except that one of its clusters 402 is mutated by replacing it with a randomly created cluster 402. (3) Crossover. Two genes have their n-tuples of clusters 402 paired off and one cluster 402 is selected at random from each pair to form the new gene. Alternatively, a new gene is created by selecting N clusters 402 at random from the 2N clusters 402 specified by the two old genes. USER INTERFACE
- Figure 6 shows an example ej ⁇ lorer user interface screen as viewed by an operator. While the invention is described primarily with regard to a specific user interface, it would be clear to those of ordinary skill in the art that another user interface of equal or greater flexibility would be suitable, and would be within the scope and spirit of the invention.
- the user interface 109 may be combined with a user interface for a generalized file system exploration program, such as in the Windows NT system referred to herein.
- the user interface 109 may comprise a query window 601 in which the operator may enter the query 104 in free text, and a results window 602 in which the system 101 may display a set of matched objects 106 found in response to the query 104.
- the operator 108 may enter the query 104 in the query window 601.
- the query 104 is input to the explorer 105, which processes it as described herein, and generates the vector query 112.
- the vector query 112 is input to the object file system 107, and generates the hit table 113 of matched objects 106.
- the hit table 113 is input to the user interface 109, which displays the matched objects 106.
- the operator may select a displayed matched object 106 to view its contents.
- the user interface 109, the explorer 105, and the object file system 107 may operate as asynchronously as possible.
- the object file system 107 may search the database 102 for matched objects 106 independently, once it has sufficient information from the ej ⁇ lorer 105; the user interface 109 may display matched objects 106 from the hit table 113 as they are generated by the object file system 107.
- the operator 108 has entered the query 104 "who invented the light bulb?" in a content field 603 of the query window 601, and the system 101 has responded with a set of matched objects 106 in the results window 602.
- the matched objects are displayed one per line, in columns labelled "rank”, “query”, “header”, and "relevant words”.
- a rank field 604 displays the quality of match for each displayed matched object 106.
- the system 101 may order the matched objects 106 by rank. This may occur as the normal procedure, or at the request of the operator 108, e.g., by means of a "sort" command 605 in the query window 601.
- the rank field 604 may also be color-coded by value.
- a query field 606 displays the relevant words of the query which are most related to the displayed matched object 106.
- a header field 607 displays the header words 204 of the displayed matched object 106.
- a relevant words field 608 displays the most common relevant words 211 of the displayed matched object 106.
- a topics field 609 of the query window 601 displays suggested topics for refinement of the query 104 which the system 101 has identified.
- the operator 108 may select a topic in the topics field 609, and the system will display a subtopics window 610 (overlaid on the query window 601 and the results window 602) showing the subtopics which the system 101 has identified for that topic.
- the operator 108 may refine the query 104 in response to the matched objects 106, and the ej ⁇ lorer 105 may attempt to match objects 106 using the query 104 as refined. This may occur at the request of the operator 108, e.g., by means of a "refresh" command 611 in the query window 601.
- the operator 108 may select one or more subtopics 405 to refine the query 104. To do so, the operator 108 may identify (e.g., by pointing to with a pointing device such as a mouse) one or more subtopics 405 in the subtopics window 610. The selected subtopics 308 may be "added" to the query 104 and the explorer 105 may attempt to match objects 106 using the query 104 as refined.
- the operator 108 may also select one or more relevant words 211 to refine the query 104. To do so, the operator 108 may identify (e.g. by pointing to) the relevant words field 608 for a particular matched object 106 and "drag" that relevant words field 608 to the content field 603; the system 101 will display a relevance feedback window 612 (overlaid on the query window 601 and the results window 602) showing the relevant words 211 for that matched object 106.
- the operator 108 may select one or more relevant words 211 to refine the query 104. To do so, the operator 108 may identify (e.g., by pointing to) one or more relevant words 211 in the relevance feedback window 612. The selected relevant words 211 may be "added" to the query 104 and the ej ⁇ lorer 105 may attempt to match objects 106 using the query 104 as refined.
- the query 104 as refined (like the original query 104) is presented as a vector query 104 to the CBR engine 110.
- selected subtopics 308 or relevant words 211 are “added” to the query, they are properties which the CBR engine 110 must match to objects 106, as described for methods of iterative refinement of case-based matching shown in those co-pending applications which have been incorporated by reference. (Thus, the CBR engine 110 must match to objects 106 as if the operator 108 had answered a query refining question in a case-based system.)
- a query 104 as refined may be further refined, allowing the operator to iteratively refine the query 104 until desired objects 106 are located.
- Figure 7 shows a second example explorer user interface screen, as viewed by an operator, in which clusters are displayed.
- the operator 108 may select a "cluster" command (figure 6) or "uncluster” (figure 7) command 701 in the query window 601, and the system 101 will display a set of clusters 402, each a set of related matched objects 106, in place of displaying matched objects 106 themselves.
- the operator has selected the "cluster" command 701 for the same query 104 as in the example of figure 6.
- an expand field 702 displays whether the cluster 402 can be expanded (shown by a "+” symbol) to display individual matched objects 106, or can be collapsed (shown by a "-" symbol) to display a single identifier for the cluster 402.
- the rank field 703 displays the best rank for all matched objects 106 in the cluster 402.
- the system 101 may order the clusters 402 by this rank field 703. This may occur as the normal procedure, or at the request of the operator 108, e.g., by means of the "sort" command 605 in the query window 601.
- this rank field 703 may also be color-coded by value.
- the relevant words field 608 displays the most common relevant words 211 in the cluster 402.
- the operator 108 may also choose to cluster all objects 106 in a specific set, e.g., a specific directory in the object file system 107.
- the operator 108 may restrict the scope of the explorer 105 to a specific directory and issue the "cluster" command 701; the system 101 will display the objects 106 in that directory in clusters 402.
- Figure 8 shows an example explorer user interface screen, as viewed by an operator, in which settings may be set by the operator.
- the operator 108 may select settings appropriate for the system 101.
- the operator 108 may select a "properties" command 801 in the query window 601 (figure 6) , and the system 101 will display a properties window 802 with a set of property values 803 which may be set.
- a "minimum rank of returned hits" property 804 is a threshold value for including matched objects 106; matched objects 106 whose rank falls below this value are not displayed in the results window 602 and are not used in further processing.
- the rank of a matched object 106 is calculated by the CBR engine 110. In the example, this value is set to 80.
- a "maximum clustered hits" property 805 is a maximum number of matched objects 106 which are included in a single cluster 402. Those matched objects 106 not included in clusters 402 are placed in a special cluster 402 labelled "Other". In the example, this value is set to 400.
- a "clustering time” property 806 is the elapsed real time devoted to clustering. In the example, this value is set to 2500 milliseconds.
- a "minimum number of clusters" property 807 is the lower bound for the number of clusters 402 generated. In the example, this value is set to 2 clusters.
- a "maximum number of clusters" property 808 is the upper bound for the number of clusters 402 generated. In the example, this value is set to 8 clusters. The system 101 attempts to generate a number of clusters 402 between the minimum and maximum number selected.
- a "maximum topics” property 809 is the maximum number of topics displayed in the topics field 609 in the query window 601. In the example, this value is set to 7 topics.
- a "maximum subtopics" property 810 is the maximum number of subtopics displayed in the subtopics window 610. In the example, this value is set to 250 subtopics.
- a "do/don't cluster” property 811 sets whether or not clustering is performed. In the example, this value is set to YES.
- a "do/don't generate query topics" property 812 sets whether or not topics and subtopics are generated in response to query terms 310. In the example, this value is set to YES.
- a "do/don't generate salient topics” property 813 sets whether or not topics and subtopics are generated in response to relevant words 211. In the example, this value is set to YES.
- a "boolean/vector query” property 814 sets whether the object file system 107 performs a boolean query or a vector query in response to the ej ⁇ lorer 105. In the example, this value is set to vector queries.
- a boolean query would have boolean connectors (e.g., "AND”, "OR”) coupling the query terms 310, so that the query 104 would not be as flexibly matched. Search using boolean queries is well known in the art.
- Appendix A shows a table of parts of speech and a set of lexical rules for the English language, which may be used for the tag-and-segment-text process or the tag-and-segment-text process in a preferred embodiment.
- Appendix B shows an output of a test run of an example filter when applied to a portion of an example multimedia encyclopedia used as a database, available as "Microsoft Encarta” from Microsoft Corporation of Redmond, Washington.
- LDOCE is basically a dictionary of British English, so we found a lot of words we wasn't familiar with, as well as a lot of double entries to account for American spellings (e.g. color and colour) .
- the lexical ⁇ categories we were able to extract out of LDOCE and WordNet were limited to nouns, verbs, adjectives, adverbs, conjunctions, determiners, predeterminers, prepositions, pronouns, and phrases. Since we don't use a phrasal lexicon, we threw the phrases away.
- noun-phrase -> determiner noun-phrase (e.g. "The person)
- noun-phrase -> quantifier noun-phrase e.g. "Three people”.
- noun-phrase -> adverb noun-phrase e.g. "maddeningly fluffy clouds"
- noun-phrase -> noun-phrase relative-clause (e.g. "The car that hit me)
- noun-phrase -> noun-phrase [, noun-phrase]* [,] or noun-phrase e.g. "England, France, or Germany
- the Find Taxonomic Relations process uses ART-IM rules to capture patterns of words which indicate taxonomic relationships between the words. For example, it detects patterns like:
- NP such as (NP.) * ⁇ (and ⁇ or) ) NP
- NP ⁇ , ⁇ including (NP,) * ⁇ (and ⁇ or) ⁇ NP
- Clustering file afl. txt Non-empty clusters 5 Clusters : 5 I Hits Vals Seed, Value: Count
- Marijuana Mixture, Leave, drugs, alcohols, syndromes, psycho Passes: 334, best pass.- 158, best score: 0.307, worst score: 0.132 Cluster 0, has 15 hits: '(OTHER), bloods, vitaminS, tissues, poisonS, suga
- Thermometer, Instrument, Measure Wine, Beverage, Juice Wood, Substance, Trunk Cluster 1 has 22 hits: 'alcohol I, acid:7, ethyl:7, liquid: , examples, chemi Acetaldehyde, Volatile, Liquid Antifreeze, Chemica1, Substance Azeotropic Mixture, Solution, Ratio Butyl Alcohol, Chemical, Formula Cannizzaro, Stanislao, Italian Disease, Medicine, Health Ester, Chemistry, Compound Ether, Chemistry, Ethyl Fermentation, Chemical, Change Formaldehyde, Compound, Carbon Glycerin, Glycerol, C3h8o3 Gum, Substance, Plant Iodine, Element, Symbol Lipid, Group, Substance Salicylic Acid, White, Solid Solution, Chemistry, Mixture Tannin, Acid, Name Turpentine, Name, Semifluid Vinegar, Condiment, Preservative Wax, Name, Ester Whiskey, Liquor, Mash Zym
- Vodka, Beverage, Known Cluster 3 has 6 hits: 'fuel:5, alcohols, methanolS, combustions, coals, en
- Rocket, Term, Propulsion Cluster 4 has 4 hits: 'drugS, alcohols, syndromes, psychoactive drugs:2, ma
- Cluster 0 has 9 hits: '(OTHER), plants, united statesS, seeds, gardenings,
- Rhizome Stem, Organ.
- Ray, Radiation, Wavelength Cluster 2 has 3 hits: 'lampS, glassS, neonS, arcS, bulbS, argonS, lights
- Neon Lamp, Glass, Bulb Cluster 3 has 5 hits: 'bulb:5, liliaceae:4, herb , lily:3, pistilS, heights.
- Tuberose, Herb, Polianth Cluster 4 has 6 hits: 'temperature:4, atmospheres, points, humidityS, bulb
- Cluster 0 has 4 hits: '(OTHER), century:2'
- Velzquez, Diego, Soldier Cluster 2 has 5 hits: 'spanish:4, island:3, spain:2, de:2, Christopher columbu
- Cluster 1 has 5 hits: 'mind:5, philosophe , philosophy:3, matters, universe
- Clustering file israel.txt Non-empty clusters: 4 Clusters: 4 II Hits Vals Seed, Value:Count
- Cluster 0 has 22 hits: '(OTHER), governments, war:4, centuryS, french revolut Achille Lauro, Italian, Cruise Anti-semi ism, Social, Agitation Asia, Continent, Island Assyria, Ashur, Ashshur Bahai, Persian, Glory Buber, Martin, Religious Cabala, Hebrew, tradition Crusade, Expedition, Undertaken Eschatology, Discourse, Last Espionage, Collection, Information Iran, Islamic Republic, Republic Jewish Art, Architect c Jew Jewish Music, Religic o , Music Nationalalism, History, Movement Portuguese Literature, Literature, Portuguese Refugee, Person, Country Romania, Republic, Europe Saudi Arabia, Monarchy, Southwest Asia
- Clustering file marx.txt Non-empty clusters: 6 Clusters: 6 ⁇ Hits Vals Seed, Value:Count
- Marx Brothers, 20th-century, Comedian Cluster 4 has 4 hits: 'capitalists, class:3, appreciation:2, communist:2, firmly
- Marx, Karl, German Cluster 5 has 6 hits: 'social 3, marx:3, labor:2, world war ii:2, german:2, ce
- Clustering file muslim.txt Non-empty clusters: 4 Clusters: 4 if Hits Vals Seed, Value:Count
- Cluster 0 has 41 hits: '(OTHER), arab:7, bc:5, ibn:4, indian:4, india:4, islam Alfonso Viii, King, Castile Arabia, Desert, Peninsula Arabic Literature, Literature, People Archaeology, Greek, Archaio Averros, Arabic, Abu
- Cluster 0 has 50 hits: "(OTHER), church:12, henry:8, king:7, english:6, roman:6
- Tyndall John, Physicist Ultrasonics, Branch, Physic Ventriloquism, Art, Sound Violin, Instrument, Member Viscount Melville Sound, Arm, Arctic Ocean Voiceprint Identification, Method, Person Warner Brothers, Motion, Picture Xylophone, Greek, Xylon Cluster 2, has 8 hits: 'sound:6, long:3, letter:3, sign:2, atlantic ocean:2, mi Animal Behavior The, Behavior, Animal C, English, Romance-language Diacritic Mark, Sign, Mark Island Sound, Body, Salt Letter, Vowel, Engli-
- Cluster 0 has 6 hits: '(OTHER), electron:2, beam:2, tube:2, television- ⁇ ' Baseball, Game, Skill Cathode-ray Tube, El*- : , Tube
- Warfare,. Use, Force Cluster 1 has 11 hits: 'strike:10, united states:3, presidents, injunctions,
- Cluster 0 has 2 hits: '(OTHER), states'
Abstract
Ce système permet d'organiser et de consulter une base de données (102) casuelle. Cette base de données (102) peut comporter un ensemble d'objets (106) tels que des documents textuels. L'organisation de la base de données (102) se fait en analysant chaque objet (106) et en lui associant un ensemble de valeurs correspondant à des propriétés, tels que des mots clés. Le document pourra être indexé soit en fonction des mots les plus fréquemment rencontrés dans le document de préférence aux mots les plus fréquemment rencontrés dans l'ensemble de la base de données, soit en fonction des mots du début du texte ou enfin en fonction des mots du titre. Le système réagit aux requêtes (104) en faisant correspondre à la requête un ensemble similaire de valeurs représentant des propriétés et en rechercher des objets similaires en fonction des correspondances casuelles des objets (106) similaires de la base de données. La requête (104) peut se faire en langage naturel et utiliser des mots clés. Le système peut proposer les objets issus de la recherche sous forme d'une réponse à la requête, peut réagir à un resserrement du champ de recherche, et peut classer les objets en fonction de la qualité de concordance. Le système peut également réagir au résultat qu'il obtient en organisant les objets répondant à la recherche en vue d'une présentation qui permette de resserrer le champ de recherche de la requête.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU73236/94A AU7323694A (en) | 1993-07-07 | 1994-07-05 | Case-based organizing and querying of a database |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US8830793A | 1993-07-07 | 1993-07-07 | |
US08/088,307 | 1993-07-07 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO1995002221A1 true WO1995002221A1 (fr) | 1995-01-19 |
Family
ID=22210607
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US1994/007569 WO1995002221A1 (fr) | 1993-07-07 | 1994-07-05 | Organisation et consultation d'une base de donnees casuelle |
Country Status (2)
Country | Link |
---|---|
AU (1) | AU7323694A (fr) |
WO (1) | WO1995002221A1 (fr) |
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