CA2932401A1

CA2932401A1 - Systems and methods for in-memory database search

Info

Publication number: CA2932401A1
Application number: CA2932401A
Authority: CA
Inventors: Scott Lightner; Franz Weckesser; Rakesh Dave; Sanjay Boddhu; Joseph Becknell; Birali HAKIZUMWAMI
Original assignee: Qbase LLC
Current assignee: Qbase LLC
Priority date: 2013-12-02
Filing date: 2014-12-02
Publication date: 2015-06-11
Also published as: KR20160124079A; WO2015084759A1; EP3077918A4; CN106164889A; EP3077918A1; JP2017504105A

Abstract

Disclosed are systems and methods identifying related entities using entity co-occurrence knowledgebase. Embodiments extract entities identified in search queries using an entity co-occurrence knowledgebase of extracted entities from an entity-indexed corpus, to present search results as related entities. Also disclosed are embodiments for generating search suggestions using fuzzy-score matching with entity co-occurrence knowledgebase. Embodiments extract partial-entities from search queries, execute matching algorithms based on types of extracted entities, and performs searches against entity co-occurrence knowledgebase. Also disclosed are embodiments for generating search suggestions of related entities based on co-occurrence and/or fuzzy-score matching. Embodiments process partial search queries and present suggestions of complete queries, which are used as new search queries. Also disclosed are embodiments for generating search suggestions using entity co¬ occurrence by extracting entities from search queries using entity and trends co-occurrence knowledgebase. Also disclosed are embodiments for enabling geographic and named-entity based searches search capabilities in contentment management systems.

Description

SYSTEMS AND METHODS FOR IN-MEMORY DATABASE SEARCH
TECHNICAL FIELD
[0001] The present disclosure relates generally to methods and systems for information retrieval; more specifically, a method for searching for related entities using entity co-occurrence. The present disclosure relates generally to query enhancement; more specifically, search suggestions using fuzzy-score matching and entity co-occurrence in a knowledge base. The present disclosure relates generally to computer query processing;
more specifically, electronic search suggestions of related entities based on co-occurrence and/or fuzzy score matching. The present disclosure relates generally to methods and systems for information retrieval; more specifically, a method for obtaining search suggestions. The present disclosure generally relates to search engines and content management; more specifically, extending a content management system's search engine technology to enable geotagging and named entities enrichment of digital content.
BACKGROUND

[0002] In the commercial context, a well known search engine parses a set of search terms and returns a list of items (web pages in a typical search) that are sorted in some manner. Most known approaches, to perform searches, are usually based on historical references of other users to build a search query database that may be eventually used to generate indexes based on keywords. User search queries may include one or more entities identified by name or attributes that may be associated with the entity.
Entities may also include organizations, people, location, date and/or time. In a typical search, if a user is searching for information related to two particular organizations, a search engine may return assorted results that may be about a mixture of different entities with the same name or similar names. The latter approach may lead the user to find a very large amount of documents that may not be relevant to what the user is actually interested.

[0003] Thus, a need exists for a method for searching for related entities that may grant the user the ability to find related entities of interest.

[0004] Users frequently use search engines for locating information of interest either on the Internet or any database system. Search engines commonly operate by receiving a search query from a user and returning search results to the user. Search results are usually ordered by search engines based on the relevance of each returned search result to the search query. Therefore, the quality of the search query may be significantly important for the quality of search results. However, search queries from users, in most cases, may be written incomplete or partial (e.g., the search query may not include enough words to generate a focused set of relevant results and instead generates a large number of irrelevant results), and sometimes misspelled (e.g., Bill Smith may be incorrectly spelled as Bill Smitth).

[0005] One common approach to improve the quality of the search results is to enhance the search query. One way to enhance the search queries may be by generating possible suggestions based on the user's input. For this, some approaches propose methods for identifying candidate query refinements for a given query from past queries submitted by one or more users. However, these approaches are based on query logs that sometimes may lead the user to results that may not be of interest. There are other approaches using different techniques that may not be accurate enough. Thus, there still exists a need for methods that improve or enhance search queries from users to get more accurate results.

[0006] Users frequently use search engines for locating information of interest either from the Internet or any database system. Search engines commonly operate by receiving a search query from a user and returning search results to the user. Search results are usually ordered based on the relevance of each returned search result to the search query. Therefore, the quality of the search query may be significantly important for the quality of search results.

However, search queries from users, in most cases, may be written incomplete or partial (e.g., the search query may not include enough words to generate a focused set of relevant results and instead generates a large number of irrelevant results), and sometimes misspelled (e.g., Bill Smith may be incorrectly spelled as "Bill Smitth").

[0007] One common approach to improve the quality of the search results is to enhance the search query. One way to enhance the search query may be by generating possible suggestions based on the user's input. For this, some approaches propose methods for identifying candidate query refinements for a given query from past queries submitted by one or more users. However, these approaches are based on query logs that sometimes may lead the user to results that may not be of interest. There are other approaches using different techniques that may not be accurate enough. Thus, there still exists a need for methods that improve or enhance search queries from users to get more accurate results and also present users with useful related entities of interest as they type the search query.

[0008] Search engines include a plurality of features in order to provide a forecast for user's query. Such forecast may include query auto-complete and search suggestions.
Nowadays, such forecast methods are based on historic keywords references.
Such historic references may not be accurate because one keyword could be referred to a plurality of topics in a single text.

[0009] In addition, user search queries may include one or more entities identified by name or attributes that may be associated with the entity. Entities may also include organizations, people, locations, events, date and/or time. In a typical search, if a user is searching for information related to two particular organizations, a search engine may return assorted results that may be about a mixture of different entities with the same name or similar names. The latter approach may lead the user to find a very large amount of documents that may not be relevant to what the user is actually interested.

[0010] Thus, a need exists for a method for obtaining quicker and more accurate search suggestions.

[0011] Content management and document management systems for document versioning and collaborative project management are known. One non-limiting example may be Microsoft's Sharepoint 2013 software and application suite of tools.
Microsoft SharePoint 2013 is a family of software products developed by Microsoft Corporation for collaboration, file sharing and web publishing. SharePoint 2013 may provide a user with a vast amount of content or information and it may become difficult for a user to find the most relevant information for a particular circumstance. To mitigate these issues SharePoint 2013 provides a search engine in order to assist users in finding the content that they need.
A user may enter a keyword based search query and the search engine in SharePoint 2013 may return to the user a list of the most relevant results found within the context of the SharePoint 2013 platform once the content has been indexed.

[0012] At times a user may desire to find content related to geographic entities in SharePoint 2013 or other type of entity such as organizations or people referred to within a document. SharePoint 2013 does not provide out of the box functionality to automatically extract entities from documents. Particularly, it does not support geotagging content to extract geographic entities and resolve them to a geographic location. Also, SharePoint 2013 does not support entity tagging in order to identify, disambiguate and extract named entities, such as, organizations or people in a document. However, SharePoint 2013 search may be extended to enable effective geographic searches and other entity related searches, including entity-based search facets. Previous versions of SharePoint 2013 included "FAST Search"

for SharePoint, from which it was possible to extend the content processing pipeline through sandboxed applications, but this was both slow and limited in the information it could access.

[0013] SharePoint 2013 introduces a much more open API which makes it possible to add specialized linguistics such as concept extraction, relationship extraction, geotagging, summarization and as well as sophisticated text analytics. Thus, an opportunity exists to extend the capabilities of SharePoint 2013 search engine to enable geographic and other entity based searches.
SUMMARY

[0014] A method for searching for related entities using entity co-occurrence is disclosed. In one aspect of the present disclosure, the method may be employed in a search system that may include a client/server type architecture. In one embodiment, the search system may include a user interface for a search engine in communication with one or more server devices over a network connection. The server device may include an entity indexed corpus of electronic data, an entity co-occurrence knowledge base database, and an entity extraction computer module. The knowledge base may be built as an in-memory database and may also include other components such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module. One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

[0015] In one embodiment, a computer-implemented method comprises receiving, by an entity extraction computer, from a client computer a search query comprising one or more entities; comparing, by the entity extraction computer, each respective entity with one or more co-occurrences of the respective entity in a co-occurrence database;
extracting, by the entity extraction computer, a subset of the one or more entities from the search query responsive to determining each respective entity of the subset exceeds a confidence score of the co-occurrence database based on a degree of certainty of co-occurrence of the entity with one or more related entities in an electronic data corpus according to the co-occurrence database; assigning, by the entity extraction computer, an index identifier (index ID) to each of the entities in the plurality of extracted entities; saving, by the entity extraction computer, the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities; searching, by a search server computer, the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur; and building, by the search server computer, a search result list having data records corresponding to the identified index IDs.

[0016] In one embodiment, a system comprising one or more server computers having one or more processors executing computer readable instructions for a plurality of computer modules including: an entity extraction module configured to receive user input of search query parameters, the entity extraction module being further configured to: extract a plurality of entities from the search query parameters by comparing each entity in the plurality of extracted entities with an entity co-occurrence database that includes a confidence score indicative of a degree of certainty of co-occurrence of an extracted entity with one or more related entities in an electronic data corpus, assign an index identifier (index ID) to each of the entities in the plurality of extracted entities, save the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities; and a search server module configured to search the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur, the search server module being further configured to build a search result list having data records corresponding to the identified index IDs.

[0017] In another embodiment, a non-transitory computer readable medium having stored thereon computer executable instructions comprising: receiving, by an entity extraction computer, user input of search query parameters; extracting, by the entity extraction computer, a plurality of entities from the search query parameters by comparing each entity in the plurality of extracted entities with an entity co-occurrence database that includes a confidence score indicative of a degree of certainty of co-occurrence of an extracted entity with one or more related entities in an electronic data corpus; assigning, by the entity extraction computer, an index identifier (index ID) to each of the entities in the plurality of extracted entities; saving, by the entity extraction computer, the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities;
searching, by a search server computer, the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur; and building, by the search server computer, a search result list having data records corresponding to the identified index IDs.

[0018] A method for generating search suggestions by using fuzzy-score matching and entity co-occurrence in a knowledge base is disclosed. In one aspect of the present disclosure, the method may be employed in a search system that may include a client/server type architecture. In one embodiment, the search system may include a user interface to a search engine in communication with one or more server devices over a network connection.
The server device may include an entity extraction computer module, a fuzzy-score matching computer module, and an entity co-occurrence knowledge base database. The knowledge base may be built as an in-memory database and may also include other hardware and/or software components such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation computer module. One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

[0019] In another aspect of the present disclosure, the method may include an entity extraction module that may perform partial entity extractions from provided search queries to identify whether the search query refers to an entity, and if so, to what type of entity it refers.
Furthermore, the method may include a fuzzy-score matching module that may spawn algorithms based on the type of entity extracted and perform a search against an entity co-occurrence knowledge base. Additionally, the query text parts that are not detected as corresponding to entities are treated as conceptual features, such as topics, facts, and key phrases, that can be employed for searching the entity co-occurrence knowledge base. In an embodiment, the entity co-occurrence knowledge base includes a repository where entities may be indexed as entities to entities, entities to topics, or entities to facts among others, which facilitates the return of fast and accurate suggestions to the user to complete the search query.

[0020] In one embodiment, a method is disclosed. The method comprises receiving, by an entity extraction computer, user input of search query parameters from a user interface, extracting, by the entity extraction computer, one or more entities from the search query parameters by comparing the search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more entities in the search query parameters, and selecting, by a fuzzy-score matching computer, a fuzzy matching algorithm for searching the entity co-occurrence database to identify one or more records associated with the search query parameters, wherein the fuzzy matching algorithm corresponds to the at least one identified entity type. The method further includes searching, by the fuzzy-score matching computer, the entity co-occurrence database using the selected fuzzy matching algorithm and forming one or more suggested search query parameters from the one or more records based on the search, and presenting, by the fuzzy-score matching computer, the one or more suggested search query parameters via the user interface.

[0021] In another embodiment, a system is provided. The system includes one or more server computers having one or more processors executing computer readable instructions for a plurality of computer modules including an entity extraction module configured to receive user input of search query parameters from a user interface, the entity extraction module being further configured to extract one or more entities from the search query parameters by comparing the search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more entities in the search query parameters. The system further includes a fuzzy-score matching module configured to select a fuzzy matching algorithm for searching the entity co-occurrence database to identify one or more records associated with the search query parameters, wherein the fuzzy matching algorithm corresponds to the at least one identified entity type.
The fuzzy-score matching module being further configured to search the entity co-occurrence database using the selected fuzzy matching algorithm and form one or more suggested search query parameters from the one or more records based on the search, and present the one or more suggested search query parameters via the user interface.

[0022] A method for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching is disclosed. In one aspect of the present disclosure, the method may be employed in a computer search system that may include a client/server type architecture. In one embodiment, the search system may include a user interface to a search engine in communication with one or more server devices over a network connection.
The server device may include one or more processors executing instructions for a plurality of special purpose computer modules, including an entity extraction module and a fuzzy-score matching module, as well as an entity co-occurrence knowledge base database. The knowledge base may be built as an in-memory database and may also include other components, such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module. One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

[0023] In another aspect of the present disclosure, the method may include performing partial entity extractions, by an entity extraction module, from provided search queries to identify whether the search query refers to an entity, and if so, to determine the entity type. Furthermore, the method may include generating algorithms, by a fuzzy-score matching module, corresponding to the type of entity extracted and performing a search against an entity co-occurrence knowledge base. Additionally , the query text parts that are not detected as entities are treated as conceptual features, such as topics, facts, and key phrases that can be employed for searching the entity co-occurrence knowledge base. The entity co-occurrence knowledge base, which may already have a repository where entities may be indexed as entities to entities, entities to topics, or entities to facts, among others, may return fast and accurate suggestions to the user to complete the search query.

[0024] In a further aspect of the present disclosure, the completed search query may be used as a new search query. The search system may process the new search query, run an entity extraction, find related entities with the highest scores from the entity co-occurrence knowledge base, and present said related entities in a drop down list that may be useful for the user.

[0025] In one embodiment, a method is disclosed. The method comprises receiving, by an entity extraction computer, user input of partial search query parameters from a user interface, the partial search query parameters having at least one incomplete search query parameter, extracting, by the entity extraction computer, one or more first entities from the partial search query parameters by comparing the partial search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more first entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more first entities in the partial search query parameters, and selecting, by a fuzzy-score matching computer, a fuzzy matching algorithm for searching the entity co-occurrence database to identify one or more records associated with the partial search query parameters, wherein the fuzzy matching algorithm corresponds to the at least one identified entity type.
The method further includes searching, by the fuzzy-score matching computer, the entity co-occurrence database using the selected fuzzy matching algorithm and forming one or more first suggested search query parameters from the one or more records based on the search, presenting, by the fuzzy-score matching computer, the one or more first suggested search query parameters via the user interface, receiving by the entity extraction computer, user selection of the one or more first suggested search query parameters so as to form completed search query parameters, and extracting, by the entity extraction computer, one or more second entities from the completed search query parameters. The method further includes searching, by the entity extraction computer, the entity co-occurrence database to identify one or more entities related to the one or more second entities so as to form one or more second suggested search query parameters, and presenting, by the entity extraction computer, the one or more second suggested search query parameters via the user interface.

[0026] In another embodiment, a system is disclosed. The system comprises one or more server computers having one or more processors executing computer readable instructions for a plurality of computer modules including an entity extraction module configured to receive user input of partial search query parameters from a user interface, the partial search query parameters having at least one incomplete search query parameter, the entity extraction module being further configured to extract one or more first entities from the partial search query parameters by comparing the partial search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more first entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more first entities in the partial search query parameters. The system further includes a fuzzy-score matching module configured to select a fuzzy matching algorithm for searching the entity co-occurrence database to identify one or more records associated with the partial search query parameters, wherein the fuzzy matching algorithm corresponds to the at least one identified entity type. The fuzzy-score matching module is further configured to search the entity co-occurrence database using the selected fuzzy matching algorithm and form one or more first suggested search query parameters from the one or more records based on the search, and present the one or more first suggested search query parameters via the user interface. Additionally, the entity extraction module is further configured to receive user selection of the one or more first suggested search query parameters so as to form completed search query parameters, extract one or more second entities from the completed search query parameters, search the entity co-occurrence database to identify one or more entities related to the one or more second entities so as to form one or more second suggested search query parameters, and present the one or more second suggested search query parameters via the user interface.

[0027] A method for obtaining search suggestions related to entities using entity and feature co-occurrence is disclosed. In one aspect of the present disclosure, the method may be employed in a search system that may include a client/server type architecture.

[0028] A search system using a method which may employ entities stored in one or more servers, which may allow an entity database and a trends database.
Entities on such databases may have a score for indexing based on the higher score. Method for obtaining search suggestions may combine information stored in both databases for generating a single list of search suggestions. Trends database may provide previous search queries from one or more users in a local network and/or the Internet. Entity database may provide search suggestions based on entities extraction from a plurality of data available in a local network and/or the Internet. This list may provide a more accurate and quicker group of suggestions for the user.

[0029] In one embodiment, a computer-implemented method comprises receiving, by a computer, from a search engine a search query comprising one or more strings of data, wherein each respective entity corresponds to a subset of the one or more strings; identifying, by the computer, one or more entities in the one or more strings of data based on comparing the one or more entities against an entity database and a trends database;
identifying, by the computer, one or more features in the one or more strings of data not identified as corresponding to at least one entity; assigning, by the computer, each of the one or more features to at least one of the one or more entities based on a matching algorithm; assigning, by the computer, an extraction score to each respective entity based on a score assigned to each respective feature assigned to the respective entity; receiving, by the computer, from an entity database a first search list containing one or more entities having a score within a threshold distance from the extraction score of each respective entity;
receiving, by the computer, from a trends database a second search list containing one or more entities having a score within a threshold distance from the extraction score of each respective entity;
generating, by the computer, an aggregated list comprising the first search list and the second search list, wherein the entities of the aggregated list are ranked according to the score of each respective aggregated list; and providing, by the computer, a suggested search according to the aggregated list.

[0030] Disclosed herein are systems and methods for enabling geographic entity-based searches in content management systems, like Microsoft's SharePoint 2013t.
Embodiments described The method involves extending the SharePoint 2013 search architecture by adding a geographic tagging web service. The system includes a computer processor operatively associated with a computer memory and one or more I/O
device, in which the processor and memory are configured to operate one or more SharePoint 2013 processes. The system also includes another computer processor operatively associated with a computer memory and one or more I/O devices, in which the processor and memory are configured to host and provide processing for a geotagging web service. The SharePoint 2013 system may include a crawling component, a content processing component and a search indexing component in order to enable search of content. The content processing component in SharePoint 2013 search may extend its functionality by using the Content Enrichment Web Service (CEWS) feature.

[0031] The method involves crawling content from the different sources in order to obtain an array of crawled properties that are sent for content processing.
During content processing, a trigger condition may determine if crawled properties may benefit from additional processing in order to enrich the original content with additional geographic metadata properties. If the crawled properties don't benefit from additional processing the crawled properties may be mapped to managed processing and sent to a search index. If the crawled properties benefit from external web services processing, the CEWS may make a simple object access protocol (SOAP) request to a configurable endpoint using hypertext transfer protocol (HTTP) or any other web service call method. An entity enrichment service may determine the type of content. If the content is in an image format, its metadata such as file location may be sent to an optical character recognition (OCR) engine so that the original document can be retrieved and processed asynchronously to convert to text and sent back to the crawl component to be re-crawled in text format. If the content is in text format the geotagging web service may identify geographic metadata and associate it with the content as managed properties. After the content has been geotagged, it may be sent to the indexing component.

[0032] An additional search user interface (UI) may be added using either SharePoint 2013 web parts or by modifying the standard layout of SharePoint 2013 search with standard web development tools such as HTML, HTML 5, JavaScript and CSS among others.
The search UI may assist a user in performing geographic search queries or displaying geographic search results using digital geographic features such as for example and without limitation, digital maps. The search UI can also be enhanced to perform faceted search using the additional enriched entities or their associated metadata.

[0033] Numerous other aspects, features and benefits of the present disclosure may be made apparent from the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present disclosure can be better understood by referring to the following figures. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the disclosure. In the figures, reference numerals designate corresponding parts throughout the different views.

[0035] FIG. 1 is a block diagram illustrating an exemplary environment of a computer system in which one embodiment of the present disclosure may operate;

[0036] FIG. 2 is a flowchart illustrating a method for searching using entity co-occurrence, according to an embodiment; and

[0037] FIG. 3 is a flowchart illustrating an embodiment of a simple search where the search results returned by the system may include related entities of interest.

[0038] FIG. 4 is a block diagram illustrating an exemplary system environment in which one embodiment of the present disclosure may operate;

[0039] FIG. 5 is a flowchart illustrating a method for search suggestions using fuzzy-score matching and entity co-occurrence in a knowledge base, according to an embodiment;
and

[0040] FIG. 6 is a diagram illustrating an example of a user interface through which a search suggestion may be produced using fuzzy matching and entity co-occurrence in a knowledge base of FIGS. 4-6.

[0041] FIG. 7 is a block diagram illustrating an exemplary system environment in which one embodiment of the present disclosure may operate.

[0042] FIG. 8 is a flowchart illustrating a method for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching, according to an embodiment.

[0043] FIG. 9 is an example embodiment of a user interface associated with the method described in FIG. 8.

[0044] FIG. 10 is a block diagram illustrating a method for obtaining search suggestions based on entities and trends databases.

[0045] FIG. 11 is a block diagram illustrating a method for obtaining search suggestions based on entities and trends databases, by generating a list of suggestions based on an individual score of search suggestions in each databases.

[0046] FIG. 12 is a block diagram illustrating a method for obtaining search suggestions based on entities and trends databases, by generating a list of suggestions based on an overall score of search suggestions on both databases.

[0047] FIG. 13 is a system architecture for tagging and entity enrichment of content in a content management system.

[0048] FIG. 14 is a process by which content is tagged and indexed for named and geographic entity searches.
DEFINITIONS

[0049] As used here, the following terms may have the following definitions:

[0050] "Entity Extraction" refers to information processing methods for extracting information such as names, places, and organizations.

[0051] "Corpus" refers to a collection of one or more documents

[0052] "Features" is any information which is at least partially derived from a document.

[0053] "Event Concept Store" refers to a database of Event template models.

[0054] "Event" refers to one or more features characterized by at least the features' occurrence in real-time.

[0055] "Event Model" refers to a collection of data that may be used to compare against and identify a specific type of event.

[0056] "Module" refers to a computer or software components suitable for carrying out at least one or more tasks.

[0057] "Feature attribute" refers to metadata associated with a feature;
for example, location of a feature in a document, confidence score, among others.

[0058] "Fact" refers to objective relationships between features.

[0059] "Entity knowledge base" refers to a computer database containing features/entities.

[0060] "Query" refers to a computer generated request to retrieve information from one or more suitable databases.

[0061] "Topic" refers to a set of thematic information which is at least partially derived from a corpus.

[0062] "Geotagging" refers to the process of extracting geographic entities from unstructured text files. Geotagging may include disambiguating the entity to a specific geographic place and appending geographic metadata such as geographic coordinates, geographic feature type and other metadata.

[0063] "Entity Tagging" refers to the process of extracting named entities from unstructured text. Entity Tagging may include entity disambiguation, entity name normalization and appending entity metadata.

[0064] "Named Entity" refers to a person, organization or topic.

[0065] "Geographic Entity" refers to geographic location or geographic places.

[0066] "Crawled Properties" refers to content management system metadata obtained from inspecting documents during crawls.
DETAILED DESCRIPTION

[0067] Reference will now be made in detail to the preferred embodiments, examples of which are illustrated in the accompanying drawings. The embodiments described above are intended to be exemplary. One skilled in the art recognizes that numerous alternative components and embodiments may be substituted for the particular examples described herein and still fall within the scope of the invention. Other embodiments may be used and/or other changes may be made without departing from the spirit or scope of the present disclosure. The illustrative embodiments described in the detailed description are not meant to be limiting of the subject matter presented here.

[0068] It will nevertheless be understood that no limitation of the scope of the invention is thereby intended. Alterations and further modifications of the inventive features illustrated here, and additional applications of the principles of the inventions as illustrated here, which would occur to one skilled in the relevant art and having possession of this disclosure, are to be considered within the scope of the invention.

[0069] The present disclosure describes a system and method for detecting, extracting and validating events from a plurality of sources. Sources may include news sources, social media websites and/or any sources that may include data pertaining to events.

[0070] Various embodiments of the systems and methods disclosed here collect data from different sources in order to identify independent events.

[0071] FIG. 1 is a block diagram of a search system 100 in accordance with the present disclosure. The search system 100 may include one or more client computing device comprising a processor executing software modules associated with the search system 100, which may include graphical user interfaces 102 accessing a search engine 104 communicating search queries in the form of binary data with a server device 106, over a network 108. In the exemplary embodiment, the search system 100 may be implemented in a client-server computing architecture. However, it should be appreciated that the search system 100 may be implemented using other computer architectures (e.g., a stand-alone computer, a mainframe system with terminals, an application service provider (ASP) model, a peer-to-peer model, and the like). The network 108 may comprise any suitable hardware and software modules capable of communicating digital data between computing devices, such as a local area network, a wide area network, the Internet, a wireless network, a mobile phone network, and the like. As such, it should also be appreciated that the system 100 may be implemented over a single network 108, or using a plurality of networks 108.

[0072] A user's computing device 102 may access a search engine 104, which may include software modules capable of transmitting search queries. Search queries are parameters provided to the search engine 104 indicating the desired information to retrieve.
Search queries may be provided by a user or another software application in any suitable data format (e.g., integers, strings, complex objects) compatible with the search engine's 104 parsing and processing routines. In some embodiments, the search engine 104 may be a web-based tool that is accessible through the user's computing device 102 browser or other software application, and enables users or software applications to locate information on the World Wide Web. In some embodiments, the search engine 104 may be application software modules native to the system 100, enabling users or applications to locate information within databases of the system 100.

[0073] Server device 106, which may be implemented as a single server device 106 or in a distributed architecture across a plurality of server computers, may include an entity extraction module 110, an entity co-occurrence knowledge base 112, and an entity indexed corpus 114. Entity extraction module 110 may be a computer software and/or hardware module able to extract and disambiguate independent entities from a given set of queries such as a query string, structured data and the like. Example of entities may include people, organizations, geographic locations, dates and/or time. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined.
Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0074] According to various embodiments, entity co-occurrence knowledge base 112 may be built, but is not limited to, as an in-memory computer database (not shown) and may include other components (not shown), such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation computer module. One search controller may be selectively associated with one or more search nodes.
Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

[0075] Entity co-occurrence knowledge base 112 may include related entities based on features and ranked by a confidence score. Various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed.
Entity indexed corpus 114 may include data from a plurality of sources such as the Internet having a massive corpus or live corpus.

[0076] FIG. 2 is a flowchart illustrating a method 200 for searching related entities using entity co-occurrence that may be implemented in a search system 100, such as the one described in FIG. 1. According to various embodiments, prior to start of method 200, an entity indexed corpus 114 similar to that described by FIG. 1 may have been fed with data from a plurality of sources such as a massive corpus or live corpus of electronic data (e.g., the Internet, website, blog, word-processing file, plaintext file). Entity indexed corpus 114 may include a plurality of indexed entities that may constantly update as new data is discovered.

[0077] In one embodiment, method 200 may start when a user or software application of a computing device 102 provides one or more search queries containing one or more entities to a search engine 104, in step 202. Search queries that were provided in step 202 may be processed by search system 100, from one to n, at each time. An example of a search query in step 202 may be a combination of keywords, such as a string, structured data, or other suitable data format. In this exemplary embodiment of FIG. 2, the keywords of the search query may be entities that represent people, organizations, geographic locations, dates and/or times.

[0078] Search queries from step 202 may then be processed for entity extraction, in step 204. In this step, the entity extraction module 110 may process search queries from step 202 as entities and compare them all against entity co-occurrence knowledge base 112 to extract and disambiguate as many entities as possible. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined.
Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0079] Furthermore, various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed. Once the entities are extracted and ranked based on confidence scores, an index ID, which in some cases may be a number, may be assigned in step 206 to the extracted entities.

[0080] Next, in step 208, a search based on the entities index ID
assigned in step 206 may be performed. In the search step 208, the extracted entities may be located within the entity indexed corpus 114 data by using standard indexing methods. Once the extracted entities are located, an entity association step 210 may follow. In the entity association step 210, all the data such as documents, videos, pictures, files or the like, where at least two extracted entities overlaps may be pulled from the entity indexed corpus 114.
Finally, a list of potential results is built, sorted by relevance, and presented to the user as search results, step 212. The list of results may then show only links to data where the user may find related entities of interest.

[0081] FIG. 3 is a particular example of a method 300 for searching related entities using entity co-occurrence, as discussed above in connection with FIG. 2. As described in FIG. 2, according to various embodiments, prior to the start of the method 300, an entity indexed corpus 114 similar to that described by FIG. 1, may have been fed with data from a plurality of sources such as a massive corpus or live corpus (the Internet).
Entity indexed corpus 114 may include a plurality of indexed entities that may constantly update as new data is discovered.

[0082] In this example embodiment, a user may be looking for information regarding "jobs" at the company "Apple". For this, the user may input one or more entities (e.g., search queries in step 302) through a user interface 102 which may be, but is not limited to, an interface with a search engine 104, such as the one described in FIG. 1. By a way of illustration and not by way of limitation, the user may input a combination of entities such as "Apple + Jobs". Next, the search engine 104 may generate search queries, step 302, and send these queries to server device 106 to be processed. At server device 106, entity extraction module 110 may perform an entity extraction step 304 from search queries input in step 302.

[0083] Entity extraction module 110 may then process search queries that were input in step 302, such as "Apple" and "Jobs", as entities and compare them all against entity co-occurrence knowledge base 112 to extract and disambiguate as many entities as possible.
During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0084] Furthermore, various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed. As a result, a table 306 including entity and co-occurrences may be created. Table 306 may then show the entity "apple" and its co-occurrences, which in this case, may be Apple and Jobs, Apple and Steve Jobs. The table 306 may also include Apple and organization A which may have been found relevant because Organization A is doing business with Apple and generating "jobs" in said organization A. Other co-occurrences may be found with less importance. As such, Apple and Jobs may then have the highest score (1), thus listed at the top, then Apple and Steve Jobs may have the second highest score (0.8), and finally Apple and other organization A may be at the bottom list with the lowest score (0.3).

[0085] Once the entities are extracted and ranked based on confidence scores, an index ID, which in some cases may be a number, may be assigned in step 308 to the extracted entities. Table 310 shows index IDs assigned to extracted entities. Table 310 then shows "Apple" with index ID 1, "Jobs" with index ID 2, "Steve Jobs" with index ID 3, and "Organization A" with index ID 4.

[0086] Next, a search step 312 based on the entities index ID 308 may be performed.
In the search step 312, the extracted entities such as "Apple", "Jobs", "Steve Jobs", and "Organization A", may be located within the entity indexed corpus 114 data by using standard indexing methods.

[0087] After locating extracted entities within the entity indexed corpus 114, an entity association 314 step may follow. In Entity association step 314, all the data such as documents, videos, pictures, files or the like, where at least two extracted entities overlaps may be pulled from the entity indexed corpus 114 to build a list of links as search results (step 318). By a way of illustration and not by way of limitation, table 316 shows how extracted entities may be associated to data in entity indexed corpus 114. In table 316, documents 1, 4, 5, 7, 8, and 10 show overlapping of two extracted entities, thus the links for these documents may be shown as search results in step 318.

[0088] FIG. 4 is a block diagram of a search computer system 400 in accordance with the present disclosure. The search system 400 may include one or more user interfaces 402 to a search engine 404 in communication with a server device 406 over a network 408. In this embodiment, the search system 400 may be implemented in one or more special purpose computers and computer modules referenced below, including via a client/server type architecture. However, the search system 400 may be implemented using other computer architectures (for example, a stand-alone computer, a mainframe system with terminals, an ASP model, a peer to peer model and the like). In an embodiment, the search computer system 400 includes a plurality of networks such as, a local area network, a wide area network, the internet, a wireless network, a mobile phone network and the like.

[0089] A search engine 404 may include a user interface, such as a web-based tool that enables users to locate information on the World Wide Web. Search engine 404 may also include user interface tools that enable users to locate information within internal database systems. Server device 406, which may be implemented in a single server device 406 or in a distributed architecture across a plurality of server computers, may include an entity extraction module 410, a fuzzy-score matching module 412, and an entity co-occurrence knowledge base database 414.

[0090] Entity extraction module 410 may be a hardware and/or software module configured to extract and disambiguate on-the-fly independent entities from a given set of queries such as a query string, partial query, structured data and the like.
Examples of entities may include people, organizations, geographic locations, dates and/or time.
During the extraction, one or more feature recognition and extraction algorithms may be employed.
Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined.
Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0091] Fuzzy-score matching module 412 may include a plurality of algorithms that may be selected according to the type of entity being extracted from a given search query.
The function of the algorithms may be to determine whether the given search query received via user input and other searched strings identified by the algorithm are similar to each other, or approximately match a given pattern string. Fuzzy matching may also be known as fuzzy string matching, inexact matching, and approximate matching. Entity extraction module 410 and fuzzy-score matching module 412 may work in conjunction with entity co-occurrence knowledge base 414 to generate search suggestions for the user.

[0092] According to various embodiments, entity co-occurrence knowledge base 414 may be built, but is not limited to, as an in-memory database and may include components, such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module. One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

[0093] Entity co-occurrence knowledge base 414 may include related entities based on features and ranked by a confidence score. Various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed.

[0094] FIG. 5 is a flowchart illustrating a method 500 for generating search suggestions using fuzzy-score matching and entity co-occurrence in a knowledge base.
Method 500 may be implemented in a search system 400, similar to that described by FIG. 4.

[0095] In one embodiment, method 500 may initiate when a user starts typing a search query in step 502 into a search engine interface 402, as described in FIG. 4. As the search query is typed in step 502, search system 400 may perform an on-the-fly process.
According to various embodiments, search query input in step 502 may be either complete or partial, either correctly spelled or misspelled. Followed, at search system 400, a partial entity extraction step 504 from the search query input of step 502 may be performed.
The partial entity extraction step 504 may run a quick search against entity co-occurrence knowledge base 414 to identify whether the search query that was input in step 502 is an entity, and if so, what type of entity it is. According to various embodiments, search query input of step 402 may then refer to a person, an organization, the location of a place, and a date among others.
Once the entity type of the search query input is identified, fuzzy-score matching module 412 may select a corresponding fuzzy matching algorithm, step 506. For example, if search query was identified as an entity that is referring to a person, then fuzzy-score matching module 412 may select the string matching algorithm for persons, for example, such as by extracting different components of the person's name including first, middle, last, and title. In another embodiment, if search query was identified as an entity that is referring to an organization, then fuzzy-score matching module 412 may select the string matching algorithm for organizations, which can include identifying terms like school, university, corp, inc, and the like. Fuzzy-score matching module 412 may then select the string matching algorithm that corresponds to the type of identified entity in the search query input so as to excel the search.
Once the string matching algorithm is adjusted to the type of identified entity, a fuzzy-score matching step 508 may be performed.

[0096] In fuzzy-score matching step 508, extracted entity or entities, as well as non-entities, may be searched and compared against entity co-occurrence knowledge base 414.
Extracted entity or entities may include incomplete names of persons, for example first name and the first character of the last name, abbreviations of organizations, for example "UN" that may stand for "United Nations", short forms, and nicknames among others.
Entity co-occurrence knowledge base 414 may already have registered a plurality of records indexed as an structured data, such as entity to entity, entity to topics, and entity to facts, among others.
The latter may allow fuzzy-score matching in step 508 to happen in a very fast way. Fuzzy-score matching in step 508 may use, but is not limited to, a common string metric such as Levenshtein distance, strcmp95, ITF scoring, and the like. Levenshtein distance between two words may refer to the minimum number of single-character edits required to change one word into the other.

[0097] Finally, once fuzzy-score matching step 508 finishes comparing and searching search query against all records in the entity co-occurrence knowledge base 414, the record that dominates the most or is the closest to match the given pattern string (i.e., the search query input of step 502) may be selected as first candidate for a search suggestion in step 510.
Other records with less proximity to match the given pattern string may be placed under the first candidate in a descending order. Search suggestion in step 510 may then be presented to the user in a drop down list of possible matches that the user may or may not ignore.

[0098] FIG. 6 is an example user interface 600 in accordance with the method for generating search suggestions using fuzzy-score matching and entity co-occurrence in a knowledge base, as discussed in FIGS 4-5 above. In this example, a user through a search engine interface 602, similar to that described by FIG. 4, inputs a partial query 604 in a search box 606. By a way of illustration and not by way of limitation, partial query 604 may be an incomplete name of a person such as "Michael J", as shown in FIG. 6. It may be considered a partial query 604 because the user may not have yet selected search button 608, or otherwise submitted the partial query 604 to search system 400 to perform an actual search and obtain results.

[0099] Following the method 500 (FIG.5), as the user types "Michael J", the entity extraction module 410 performs a quick search on-the-fly of the first word (Michael) against entity co-occurrence knowledge base 414 to identify the type of entity, in this example, the entity may refer to the name of a person. Consequently, fuzzy-score matching module 412 may select a string match algorithm tailored for names of persons. Name of persons may be found in databases written in different forms such as using only initials (short forms), or first name and first character of the last name, or first name, initial of the middle name and last name, or any combination thereof Fuzzy-score matching module 412 may use a common string metric such as Levenshtein distance to determine and assign a score to the entity, topic, or fact within entity co-occurrence knowledge base 414 that may match the entity "Michael".
In this example, Michael matches with a great amount of records having that name. However, as the user types the following character "J", fuzzy-score matching module 412 may perform another comparison based on Levenshtein distance against all co-occurrences with Michael with the entity co-occurrence knowledge base 414. Entity co-occurrence knowledge base 414 may then select all possible matches with the highest scores for "Michael J".
For example, fuzzy-score matching module 412 may return search suggestions 610 such as "Michael Jackson", "Michael Jordan", "Michael J. Fox", or even "Michael Dell" in some cases to the user. The user may then be able to select from the drop down list one of the persons suggested as to complete the search query. Expanding on the aforementioned example, a query like "Michael the basketball player", would lead to the suggestion of "Michael Jordan", based on the results returned by searching entity co-occurrence knowledge base for "Michael" in person entity name variations and "the basketball player" in the co-occurrence features like key phrases, facts, and topics. Another example can be "Alexander the actor", would lead to the suggestion of "Alexander Polinsky". Those skilled in the art will realize that the presently existing search platforms cannot generate suggestions in the aforementioned manner.

[0100] FIG. 7 is a block diagram of a search system 700 in accordance with the present disclosure. The search system 700 may include one or more user interfaces 702 to a search engine 704 in communication with a server device 706 over a network 708. In this embodiment, the search system 700 may be implemented in a client/server type architecture;
however, the search system 700 may be implemented using other computer architectures (for example, a stand-alone computer, a mainframe system with terminals, an ASP
model, a peer to peer model and the like) and a plurality of networks such as, a local area network, a wide area network, the internet, a wireless network, a mobile phone network and the like.

[0101] A search engine 704 may include, but is not limited to, an interface via a web-based tool that enables users to locate information on the World Wide Web.
Search engine 704 may also include tools that enable users to locate information within internal database systems. Server device 706, which may be implemented in a single server device 706 or in a distributed architecture across a plurality of server computers, may include an entity extraction module 710, a fuzzy-score matching module 712, and an entity co-occurrence knowledge base database 714.

[0102] Entity extraction module 710 may be a hardware and/or software computer module able to extract and disambiguate on-the-fly independent entities from a given set of queries such as a query string, partial query, structured data and the like.
Example of entities may include people, organizations, geographic locations, dates and/or time.
During the extraction, one or more feature recognition and extraction algorithms may be employed.
Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined.
Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0103] Fuzzy-score matching module 712 may include a plurality of algorithms that may be adjusted or selected according to the type of entity extracted from a given search query. The function of the algorithms may be to determine whether the given search query (input) and suggested searched strings are similar to each other, or approximately match a given pattern string. Fuzzy matching may also be known as fuzzy string matching, inexact matching, and approximate matching. Entity extraction module 710 and fuzzy-score matching module 712 may work in conjunction with Entity co-occurrence knowledge base 714 to generate search suggestions for the user.

[0104] According to various embodiments, entity co-occurrence knowledge base 714 may be built, but is not limited to, as an in-memory database and may include components such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module. One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

[0105] Entity co-occurrence knowledge base 714 may include related entities based on features and ranked by a confidence score. Various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed.

[0106] FIG. 8 is a flowchart illustrating an embodiment of a method 800 for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching. Method 800 may be implemented in a search system 700, similar to as described in FIG. 7.

[0107] In one embodiment, method 800 may initiate when a user starts typing a search query, step 802, in the search engine 704, as described above in FIG.
7. As the search query is typed, search system 700 may perform an on-the-fly process. According to various embodiments, search query may be complete and/or partial, correctly spelled and/or misspelled. Next, a partial entity extraction step 804 of search query may be performed. The partial entity extraction step 804 may run a quick search against entity co-occurrence knowledge base 714 to identify whether the search query includes an entity and, if so, the entity type. According to various embodiments, search query entity may refer to a person, an organization, the location of a place, and a date among others. Once the entity is, a fuzzy-score matching module 712 may select a corresponding fuzzy matching algorithm, step 806.
For example, if search query was identified as an entity that is referring to a person, then fuzzy-score matching module 712 may adjust or select the string matching algorithm for persons, which can extract different components of the person's name, including first, middle, last, and title. In another embodiment, if search query was identified as an entity that is referring to an organization, then fuzzy-score matching module 712 may adjust or select the string matching algorithm for organizations, which can include identifying terms such as school, university, corp., and inc. Fuzzy-score matching module 712 therefore adjusts or selects the string matching algorithm for the type of entity in order to facilitate the search.
Once the string matching algorithm is adjusted or selected to correspond to the type of entity, a fuzzy-score matching may be performed in step 808.

[0108] In fuzzy-score matching step 808, extracted entity or entities, as well as any non-entities, may be searched and compared against entity co-occurrence knowledge base 714. Extracted entity or entities may include incomplete names of persons, for example first name and the first character of the last name, abbreviations of organizations, for example "UN" that may stand for "United Nations", short forms, and nicknames among others. Entity co-occurrence knowledge base 714 may already have registered a plurality of records indexed in an structured data, such as entity to entity, entity to topics, and entity to facts index among others. This may allow fuzzy-score matching in step 808 to happen expeditiously. Fuzzy-score matching may use, but is not limited to, a common string metric such as Levenshtein distance, strcmp95, ITF scoring, and the like. Levenshtein distance between two words may refer to the minimum number of single-character edits required to change one word into the other.

[0109] Once fuzzy-score matching in step 808 step finishes comparing and searching the search query against all records in the entity co-occurrence knowledge base 714, the record that dominates the most or is the closest to match the given pattern string of the search query input may be selected as first candidate for a search suggestion, step 810. Other records with less proximity to match the given pattern string of the search query input may be placed under the first candidate in a descending order. Search suggestion in step 810 may then be presented to the user in a drop down list of possible matches that the user may select to complete the query.

[0110] In another embodiment, after the user selects a match of his/her interest, search system 700 may take that selection as a new search query, step 812.
Subsequently, an entity extraction step 814 from said new search query may be performed. During the extraction, one or more feature recognition and extraction algorithms may be employed.
Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined.
Additionally, the relevance of the association between features may be determined using a weighted scoring model. Entity extraction module 710 may then run a search against entity co-occurrence knowledge base 714 to find related entities, step 816, based on the co-occurrences with the highest scores. Finally, a drop down list of search suggestions, in step 818, including related entities, may be presented to the user before performing the actual search of the data in the electronic document corpus.

[0111] FIG. 9 is an example embodiment of a user interface 900 associated with the method 800 for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching. In this example, a user through a search engine interface 902, similar to that described by FIG. 7, inputs a partial query 904 in a search box 906. By a way of illustration and not by way of limitation, partial query 304 may be an incomplete name of a person such as "Michael J", as shown in FIG. 9. It may be considered a partial query 904 because the user may not have yet selected search button 908, or otherwise submitted the partial query 904 to search system 100 to perform an actual search and obtain results.

[0112] Following the method 800, as the user types "Michael J", the entity extraction module 710 performs a quick search on-the-fly of the first word (Michael) against entity co-occurrence knowledge base 714 to identify the type of entity, in this example, the entity may refer to the name of a person. Subsequently, fuzzy-score matching module 712 may select a string match algorithm tailored for names of persons. Name of persons may be found in databases written in different forms such as using only initials (short forms), or first name and first character of the last name, or first name, initial of the middle name and last name, or any combination thereof Fuzzy-score matching module 712 may use a common string metric such as Levenshtein distance to determine and assign a score to the entity, topic, or fact within entity co-occurrence knowledge base 714 that may match the entity "Michael". In this example, Michael matches with a great amount of records having that name.
However, as the user types the following character "J", fuzzy-score matching module 712 may perform another comparison based on Levenshtein distance against all co-occurrences with Michael with the entity co-occurrence knowledge base 714. Entity co-occurrence knowledge base 714 may then select all possible matches with the highest scores for "Michael J".
For example, fuzzy-score matching module 712 may return search suggestions 910 to complete "Michael J" such as "Michael Jackson", "Michael Jordan", "Michael J. Fox", or even "Michael Dell" in some cases to the user. The user may then be able to either select from the drop down list one of the persons suggested, or ignore the suggestion and keep typing. Expanding on the aforementioned example, a query like "Michael the basketball player", would lead to the suggestion of "Michael Jordan", based on the results returned by searching Entity co-occurrence knowledge base for "Michael" in person entity name variations and "the basketball player" in the co-occurrence features like key phrases, facts, topics, and the like.
Another example can be "Alexander the actor", would lead to the suggestion of "Alexander Polinsky". As those skilled in the art will realize, the existing search platforms cannot provide suggestions generated in the aforementioned manner.

[0113] In this embodiment, the user may select "Michael Jordan" from the drop down list to complete the partial query 904, as indicated in FIG. 9. Said selection may then be processed as a new search query 912 by search system 700. Subsequently, an entity extraction from said new search query 912 may be performed. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined.
Additionally, the relevance of the association between features may be determined using a weighted scoring model. Entity extraction module 710 may then run a search for "Michael Jordan"
against entity co-occurrence knowledge base 714 to find related entities based on the co-occurrences with the highest scores. Finally, a drop down list of search suggestions 914, including related entities, may be presented to the user before performing the actual search by clicking on the search button 908. The foregoing system and method described in FIGS. 7-9 may be fast and convenient for the user since the user may find useful relationships.

[0114] FIG. 10 is a block diagram of a search system 1000 in accordance with the present disclosure. The search system 1000 may include a search engine 1002, such search engine 1002 may include one or more user interfaces allowing data input from the user, such as user queries.

[0115] Search system 1000 may include one or more databases. Such databases may include entity database 1004 and trends database 1006. Databases may be stored in a local server or in a web based server. Thus, search system 1000 may be implemented in a client/server type architecture; however, the search system 1000 may be implemented using other computer architectures, for example, a stand-alone computer, a mainframe system with terminals, an ASP model, a peer to peer model, and the like, and a plurality of networks such as, a local area network, a wide area network, the internet, a wireless network, a mobile phone network, and the like.

[0116] A search engine 1002 may include, but is not limited to, a web-based tool that enables users to locate information on the World Wide Web. Search engine 1002 may also include tools that enable users to locate information within internal database systems.

[0117] Entity database 1004, which may be implemented as a single server or in a distributed architecture across a plurality of servers. Entity database 1004 may allow a set of entities queries, such as a query string, structured data and the like. Such set of entities queries may be previously extracted from a plurality of corpus available in the internet and/or local network. Entities queries may be indexed and scored. Example of entities may include people, organizations, geographic locations, dates and/or time. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined.
Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0118] Trends database 1006, which may be implemented as a single server or in a distributed architecture across a plurality of servers. Trends database 1006 may allow a set of entities queries, such as a query string, structured data, and the like. Such set of entities queries may be previously extracted from historical queries performed by the user and/or a plurality of users in the intern& and/or local network. Entities queries may be indexed and scored. Example of entities may include people, organizations, geographic locations, dates and/or time. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0119] Entity database 1004 and trends database 1006 may include entity co-occurrence knowledge base, which may be built, but is not limited to, as an in-memory database (not shown) and may include other components (not shown), such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module. One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

[0120] Co-occurrence knowledge base may include related entities based on features and ranked by a confidence score. Various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed.

[0121] Search system 1000 may compare user query at search engine 1002 against entity database 1004 and trends database 1006. Auto-complete mode on search engine 1002 may be enabled from both databases; entity databases 1004 and trends databases 1006.
Search system 1000 may deploy a list of search suggestions 1008 to the user, such list may be generated and indexed based on a fuzzy score assigned to each entity suggestion in databases.
Score of each entity suggestion may be assigned automatically by the search system 1000 and/or manually by a system supervisor. Entities suggestion may be ordered from the most relevant to the less relevant based on the score achieved by each entity. In addition, score in trends database 1006 may be assigned using trends and query frequency from one or more users in a local network and/or Internet.

[0122] Entity suggestion of each database may be compared among them and then indexed and ordered by the rank obtained in the score, thus a list of search suggestions 1008 may be shown to user combining entity suggestions in both databases; entity database 1004 and trends database 1006. If user select a suggestion from the list or select another result out of the suggestion list, then search system 1000 may save such information in trends database 1006. Thus, a self-learning system may be allowed, which may increase search system 1000 reliability and accuracy. In brief, the trends co-occurrence knowledge base can be continuously updated, with the features extracted from the user's query and the selected suggestions, providing a means of on-the-fly learning, which improves the search relevancy and accuracy. Further, trends co-occurrence knowledge base can be populated by the different users using the system and also by automatic methods like trend detection modules.

[0123] FIG. 11 is a block diagram of a search system 1100 in accordance with the present disclosure. The search system 1100 may include a search engine 1102, such search engine 1102 may include one or more user interfaces allowing data input from the user, such as user queries.

[0124] Search system 1100 may include one or more databases. Such databases may include entity database 1104 and trends database 1106. Databases may be stored in a local server or in a web based server. Thus, search system 1100 may be implemented in a client/server type architecture; however, the search system 1100 may be implemented using other computer architectures, for example, a stand-alone computer, a mainframe system with terminals, an ASP model, a peer to peer model, and the like, and a plurality of networks such as, a local area network, a wide area network, the internet, a wireless network, a mobile phone network, and the like.

[0125] In one embodiment, search system 1100 may start when a user inputs one or more entities (in search queries) through a user interface in search engine 1102. An example of a search query may be a combination of keywords in a string data format, structured data, and the like. These keywords may be entities that represent people, organizations, geographic locations, dates and/or time. In the present embodiment, "Indiana Na" is used as search query.

[0126] "Indiana Na" may then be processed for entity extraction. An entity extraction module may process search queries such as, "Indiana Na" as entities and compare them all against entity co-occurrence knowledge base in entity database 1104 and trends database 1106 to extract and disambiguate as many entities as possible. Additionally, the query text parts that are not detected as entities (e.g., person, organization, location), are treated as conceptual features (e.g., topics, facts, key phrases) that can be employed for searching the entity co-occurrence knowledge bases (e.g., entity and trend databases).
During the extraction, one or more feature recognition and extraction algorithms may be employed.
Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined.
Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0127] In the present embodiment, entity database 1104 may show a list of search suggestions, as a list of entity suggestions 1108, which may be indexed and ranked. Trends database 1106 may show a list of search suggestions, as trends based suggestion list 1110, which may be indexed and ranked. Subsequently, search system 1100 may build a search suggestions list 1112 based on those provided by entity database 1104 and trends database 1106. The search suggestions list 1112 may be indexed and ranked based on the individual score of each entity suggestion in each database; thus, the most relevant may be shown first and the less relevant result may continue below it.

[0128] In search system 1100, an exemplary use for obtaining search suggestion is disclosed. Search suggestions list 1112 may show suggestions based on "Indiana Na" user query. As a result, "Indiana Name" may appear first based on an individual score of 0.9 for that entity, then "Indiana Nascar" may be shown as a result of an individual score of 0.8, finally "Indiana Nashville" may be shown based on an individual score of 0.7.
The individual score may be compared using list of entity suggestions 1108 and trends based suggestion list 1110, without applying considering repeated entities.

[0129] FIG. 12 is a block diagram of a search system 1200 in accordance with the present disclosure. Search system 1200 may include a search engine 1202, such search engine 1202 may include one or more user interfaces allowing data input from the user, such as user queries.

[0130] Search system 1200 may include one or more databases. Such databases may include entity database 1204 and trends database 1206. Databases may be stored in a local server or in a web based server. Thus, search system 1200 may be implemented in a client/server type architecture; however, the search system 1200 may be implemented using other computer architectures; for example, a stand-alone computer, a mainframe system with terminals, an ASP model, a peer to peer model, and the like, and a plurality of networks such as, a local area network, a wide area network, the internet, a wireless network, a mobile phone network, and the like.

[0131] In one embodiment, search system 1200 may start when a user inputs one or more entities (search queries) through a user interface in search engine 1202.
An example of a search query may be a combination of keywords such as a string, structured data and the like. These keywords may be entities that represent people, organizations, geographic locations, dates and/or time. In the present embodiment, "Indiana Na" is used as search query.

[0132] "Indiana Na" may then be processed for entity extraction. An entity extraction module may process search queries such as, "Indiana Na," as entities and compare them all against entity co-occurrence knowledge base in entity database 1204 and trends database 1206 to extract and disambiguate as many entities as possible. Additionally, the query text parts that are not detected as entities (e.g., person, organization, location), are treated as conceptual features (e.g., topics, facts, key phrases), which may be employed for searching the entity co-occurrence knowledge bases (e.g., entity database, trend databases). During the extraction, one or more feature recognition and extraction algorithms may be employed.
Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Based on the respective feature attributes, the relative weight and/or the relevance of each of the features, may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.

[0133] In the present embodiment, entity database 1204 may show a list of search suggestions, list of entity suggestions 1208, which may be already indexed and ranked.
Equally, trends database 1206 may show a list of search suggestions, trends based suggestion list 1210, which may be already indexed and ranked. Subsequently, search system 1200 may build a search suggestions list 1212 based on those provided by entity database 1204 and trends database 1206. The search suggestions list 1212 may be indexed and ranked based on the overall score of each entity suggestion in both databases, thus, the most relevant may be shown first and the less relevant result may continue below it.

[0134] In Search system 1200, an exemplary use for obtaining search suggestion is disclosed. Search suggestions list 1212 may show suggestions based on "Indiana Na" user query. As a result, "Indiana Nascar" may appear first based on an overall score of 1.4 resulting from the sum of score 0.8 at list of entity suggestions 1208 and score 0.6 at trends based suggestion list 1210. Similarly, "Indiana Name" may be shown as a result of an overall score of 0.9, finally "Indiana Nashville' may be shown based on an overall score of 0.7.

[0135] FIG. 13 is a system architecture 1300 for geotagging content in SharePoint 2013t. A Search index 1324 is one of a number of key components in order to enable search in SharePoint 1302. Another key part of enabling search in SharePoint 2013 1302 may be content capturing in order to index the content. SharePoint 1302 includes a crawler 1304 component in order to enable content capturing.

[0136] Crawler 1304 may crawl through different content sources 1306 adding a list of metadata properties to each content. Examples of content sources may include without limitation, SharePoint content, network file-share or user or intranet content. Crawler 1304 may be configured perform the functions of connecting securely to a content source 1306, associating document from the sources to their metadata as crawled properties.
The crawler 1304 may be configured to full or incremental crawls to content. Examples of crawled properties may include for example and without limitation author, title, creation date among others.

[0137] SharePoint 2013 includes a content processing 1308 component. The content processing 1308 component takes content from the crawler 1304 and prepares it for indexing. Content processing 1308 may involve stages of linguistic processing (language detection), parsing, entity extraction management, content-based file format detection, content processing error reporting, natural language processing and mapping crawled properties to managed properties among others.

[0138] Content processing 1308 may be extended by means of a content enrichment web service (CEWS 1310). CEWS 1310 may enable the enrichment of content processing 1308 by allowing a web service callout 1312 to call external web service to perform additional actions and enrich the crawled data properties. Web service callout 1312 may be a standard simple object access protocol (SOAP) request or any other web service call method used to exchange structured information of the crawled data with an entity enrichment service 1314. Web service callout 1312 may include trigger conditions configured in the content enrichment configuration object that control when to call an external web service for enrichment processing. Entity enrichment service 1314 may also determine the document type of the crawled data in order to determine content that may come in the form of an image (scanned documents, pictures, etc.). Whenever content in the form of an image is found the entity enrichment service 1314 may send the location of the crawled document to an OCR
processing engine 1316 such as for example and without limitation an optical character recognition component or other image processing component. OCR processing engine 1316 may then retrieve and process the image files and convert them to text files asynchronously.
The OCR'd processed files 1318 may subsequently be re-fed to crawler 1304 in order to be crawled as text files and sent back to content processing 1308 and proceed with the rest of the workflow.

[0139] System architecture 1300 may include an external geotagger web service 1320 and a named entity tagger service 1322. Both geotagger web service 1320 and named entity tagger service 1322 may be a software module configured to function as a web service application provider and to respond to web service callout 1312. Geotagger web service 1320 may use natural language processing entity extraction techniques, machine learning models and other techniques in order to identify and disambiguate geographic entities from crawled content. For example, geotagger web service 1320 may disambiguate geographic entities by analyzing statistical co-occurrence of entities found in a gazetteer.
Geotagger web service 1320 may include a database of statistical co-occurring entities which may be linked against content found by crawler 1304. Following the same technique, named entity tagger service 1322 may be used to extract additional entities or text features such as organizations, people or topics.

[0140] Geotagger web service 1320 may analyze an array of managed properties sent as input properties by CEWS 1310 and identify any geographic entities referred in text. Non-limiting examples of input properties may include: FileType, IsDocument, OriginalPath and body among others. Geotagger web service 1320 may then geotag the text by creating or modifying managed properties with reference to each geographic entity found.
Geotagger web service 1320 may send modified or new managed properties to the entity enrichment service 1314 where a conversion is made that maps the modified managed properties and returns them as output properties back to CEWS 1310. The same process may be used to interact with the named entity tagger service 1322 for the extraction and entity tagging of other entities or text features such as organizations, people or topics.

[0141] After the augmented managed properties are returned by the entity enrichment service 1314 the properties are merged with the crawled file managed properties and sent to a search index 1324.

[0142] Once geographic and other entity tags have been associated with content and indexed, search queries may also be performed using geographic or named entity features. A
search UI 1326 in SharePoint 2013 may include specific displays that may assist a user in performing a geographic based search as well as support enhanced displays of faceted search results. The search UI 1326 may be a custom web part or may also be done by modifying the standard layout of SharePoint 2013 search with standard tools such as HTML, HTML 5, JavaS cript and C S S .

[0143] FIG. 14 is a flow chart 1400 illustrating the process steps for tagging content for SharePoint 2013 search. The process may begin when the crawler component in SharePoint 2013 performs a crawl for content (step 1402). In one embodiment the crawl may be a full crawl, wherein in another embodiment the crawl may be an incremental crawl.
The crawler component may then feed crawled properties and metadata to the content processing (step 1404). A determination is made to verify if the crawled content may include geographic or named entities. For example and without limitation a trigger condition may be used. The trigger condition may contain a set of programmatic logic or rules which may determine if content may benefit from geotagging or entity tagging. If the trigger condition evaluates to false crawled content may be associated with managed properties (step 1406) and passed to the search index component (step 1408). If the trigger condition evaluates to true the CEWS may send a web service callout (step 1410) to an entity enrichment service.
The entity enrichment service may analyze the content sent in order to determine if the content may be in an image format (scanned documents, pictures, etc.). Content found in an image format may be processed asynchronously by an OCR engine and sent back to be re-crawled by the crawling component as text files (step 1412). If the content is not in image format, the content may be processed by a geotagging web service or a name entity tagger service (step 1414). The web service may extract and disambiguate geographic or named entities referred in the content and enrich them with entity metadata. The identified entities and their metadata may be sent back as managed properties to the content processing component and associated with the content (step 1416). The associated metadata may then be sent to the search index component (step 1406).

[0144] While various aspects and embodiments have been disclosed, other aspects and embodiments are contemplated. The various aspects and embodiments disclosed are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

[0145] The foregoing method descriptions and the process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the steps in the foregoing embodiments may be performed in any order. Words such as "then," "next," etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods. Although process flow diagrams may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently.
In addition, the order of the operations may be re-arranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.

[0146] The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0147] Embodiments implemented in computer software may be implemented in software, firmware, middleware, microcode, hardware description languages, or any combination thereof. A code segment or machine-executable instructions may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements.
A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents.
Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

[0148] The actual software code or specialized control hardware used to implement these systems and methods is not limiting of the invention. Thus, the operation and behavior of the systems and methods were described without reference to the specific software code being understood that software and control hardware can be designed to implement the systems and methods based on the description herein.

[0149] When implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable or processor-readable storage medium. The steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module which may reside on a computer-readable or processor-readable storage medium. A non-transitory computer-readable or processor-readable media includes both computer storage media and tangible storage media that facilitate transfer of a computer program from one place to another. A non-transitory processor-readable storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such non-transitory processor-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other tangible storage medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer or processor. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non-transitory processor-readable medium and/or computer-readable medium, which may be incorporated into a computer program product.

[0150] It is to be appreciated that the various components of the technology can be located at distant portions of a distributed network and/or the Internet, or within a dedicated secure, unsecured and/or encrypted system. Thus, it should be appreciated that the components of the system can be combined into one or more devices or co-located on a particular node of a distributed network, such as a telecommunications network. As will be appreciated from the description, and for reasons of computational efficiency, the components of the system can be arranged at any location within a distributed network without affecting the operation of the system. Moreover, the components could be embedded in a dedicated machine.

[0151] Furthermore, it should be appreciated that the various links connecting the elements can be wired or wireless links, or any combination thereof, or any other known or later developed element(s) that is capable of supplying and/or communicating data to and from the connected elements. The term module as used herein can refer to any known or later developed hardware, software, firmware, or combination thereof that is capable of performing the functionality associated with that element. The terms determine, calculate and compute, and variations thereof, as used herein are used interchangeably and include any type of methodology, process, mathematical operation or technique.

[0152] The preceding description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the following claims and the principles and novel features disclosed herein.

[0153] The embodiments described above are intended to be exemplary. One skilled in the art recognizes that numerous alternative components and embodiments that may be substituted for the particular examples described herein and still fall within the scope of the invention.

Claims

What is claimed is:

1. A computer-implemented method comprising:
receiving, by an entity extraction computer, from a client computer a search query comprising one or more entities;
comparing, by the entity extraction computer, each respective entity with one or more co-occurrences of the respective entity in a co-occurrence database;
extracting, by the entity extraction computer, a subset of the one or more entities from the search query responsive to determining each respective entity of the subset exceeds a confidence score of the co-occurrence database based on a degree of certainty of co-occurrence of the entity with one or more related entities in an electronic data corpus according to the co-occurrence database;
assigning, by the entity extraction computer, an index identifier (index ID) to each of the entities in the plurality of extracted entities;
saving, by the entity extraction computer, the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities;
searching, by a search server computer, the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur; and building, by the search server computer, a search result list having data records corresponding to the identified index IDs.

2. The method of claim 1 further comprising sorting, by the search server computer, the search result list by relevance based on the confidence score and forwarding, by the search server computer, the sorted search result list to a user device.

3. The method of claim 1 wherein the plurality of extracted entities is ranked based on the confidence score.

4. The method of claim 1 wherein the entity extraction computer associates an extracted entity with one or more co-occurring entities in the entity indexed electronic data corpus.

5. The method of claim 4 wherein the associated entities are ranked by the confidence score.

6. The method of claim 1 wherein each of the plurality of entities is selected from the group consisting of a person, an organization, a geographic location, a date, and a time.

7. A system comprising:
one or more server computers having one or more processors executing computer readable instructions for a plurality of computer modules including:
an entity extraction module configured to receive user input of search query parameters, the entity extraction module being further configured to:
extract a plurality of entities from the search query parameters by comparing each entity in the plurality of extracted entities with an entity co-occurrence database that includes a confidence score indicative of a degree of certainty of co-occurrence of an extracted entity with one or more related entities in an electronic data corpus, assign an index identifier (index ID) to each of the entities in the plurality of extracted entities, save the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities; and a search server module configured to search the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur, the search server module being further configured to build a search result list having data records corresponding to the identified index IDs.

8. The system of claim 7 wherein the search server module is further configured to sort the search result list by relevance based on the confidence score and forward the sorted search result list to a user device.

9. The system of claim 7 wherein the plurality of extracted entities is ranked based on the confidence score.

10. The system of claim 7 wherein the entity extraction module is configured to associate an extracted entity with one or more co-occurring entities in the entity indexed electronic data corpus.

11. The system of claim 10 wherein the associated entities are ranked by the confidence score.

12. The system of claim 7 wherein each of the plurality of entities is selected from the group consisting of a person, an organization, a geographic location, a date, and a time.

13. A non-transitory computer readable medium having stored thereon computer executable instructions comprising:
receiving, by an entity extraction computer, user input of search query parameters;
extracting, by the entity extraction computer, a plurality of entities from the search query parameters by comparing each entity in the plurality of extracted entities with an entity co-occurrence database that includes a confidence score indicative of a degree of certainty of co-occurrence of an extracted entity with one or more related entities in an electronic data corpus;
assigning, by the entity extraction computer, an index identifier (index ID) to each of the entities in the plurality of extracted entities;
saving, by the entity extraction computer, the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities;
searching, by a search server computer, the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur; and building, by the search server computer, a search result list having data records corresponding to the identified index IDs.

14. The computer readable medium of claim 13 wherein the instructions further comprise sorting, by the search server computer, the search result list by relevance based on the confidence score and forwarding, by the search server computer, the sorted search result list to a user device.

15. The computer readable medium of claim 13 wherein the plurality of extracted entities is ranked based on the confidence score.

16. The computer readable medium of claim 13 wherein the instructions further comprise associating, by the entity extraction computer, an extracted entity with one or more co-occurring entities in the entity indexed electronic data corpus.

17. The computer readable medium of claim 16 wherein the associated entities are ranked by the confidence score.

18. The computer readable medium of claim 13 wherein each of the plurality of entities is selected from the group consisting of a person, an organization, a geographic location, a date, and a time.

19. A method comprising:
receiving, by an entity extraction computer, user input of search query parameters from a user interface;
extracting, by the entity extraction computer, one or more entities from the search query parameters by comparing the search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more entities in the search query parameters;
selecting, by a fuzzy-score matching computer, a fuzzy matching algorithm for searching the entity co-occurrence database to identify one or more records associated with the search query parameters, wherein the fuzzy matching algorithm corresponds to the at least one identified entity type;
searching, by the fuzzy-score matching computer, the entity co-occurrence database using the selected fuzzy matching algorithm and forming one or more suggested search query parameters from the one or more records based on the search; and presenting, by the fuzzy-score matching computer, the one or more suggested search query parameters via the user interface.

20. The method of claim 19 further comprising searching, by the fuzzy-score matching computer, the entity co-occurrence database using the selected fuzzy matching algorithm before the user input is finalized.

21. The method of claim 19 wherein the one or more records associated with the search query parameters include conceptual features.

22. The method of claim 19 wherein the one or more suggested search query parameters include a plurality of suggested search query parameters, the method further comprising sorting, by the fuzzy-score matching computer, the plurality of suggested search query parameters in descending order based on proximity of a match to the search query parameters in the user input.

23. The method of claim 22 wherein the fuzzy-score matching computer presents the sorted plurality of suggested search query parameters in a drop down list via the user interface.

24. The method of claim 19 wherein the entity co-occurrence database is indexed.

25. The method of claim 1 wherein the entity co-occurrence database includes an entity to entity index.

26. The method of claim 19 wherein the entity co-occurrence database includes an entity to topics index.

27. The method of claim 19 wherein the entity co-occurrence database includes an entity to facts index.

28. A system comprising:
one or more server computers having one or more processors executing computer readable instructions for a plurality of computer modules including:
an entity extraction module configured to receive user input of search query parameters from a user interface, the entity extraction module being further configured to:
extract one or more entities from the search query parameters by comparing the search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more entities in the search query parameters; and a fuzzy-score matching module configured to select a fuzzy matching algorithm for searching the entity co-occurrence database to identify one or more records associated with the search query parameters, wherein the fuzzy matching algorithm corresponds to the at least one identified entity type, the fuzzy-score matching module being further configured to:
search the entity co-occurrence database using the selected fuzzy matching algorithm and form one or more suggested search query parameters from the one or more records based on the search, and present the one or more suggested search query parameters via the user interface.

29. The system of claim 28 wherein the fuzzy-score matching module is further configured to search the entity co-occurrence database using the selected fuzzy matching algorithm before the user input is finalized.

30. The system of claim 28 wherein the one or more records associated with the search query parameters include conceptual features.

31. The system of claim 28 wherein the one or more suggested search query parameters include a plurality of suggested search query parameters and the fuzzy-score matching computer is further configured to sort the plurality of suggested search query parameters in descending order based on proximity of a match to the search query parameters in the user input.

32. The system of claim 32 wherein the fuzzy-score matching computer is configured to present the sorted plurality of suggested search query parameters in a drop down list via the user interface.

33. The system of claim 28 wherein the entity co-occurrence database is indexed.

34. The system of claim 28 wherein the entity co-occurrence database includes an entity to entity index.

35. The system of claim 28 wherein the entity co-occurrence database includes an entity to topics index.

36. The system of claim 28 wherein the entity co-occurrence database includes an entity to facts index.

37. A method comprising:
receiving, by an entity extraction computer, user input of partial search query parameters from a user interface, the partial search query parameters having at least one incomplete search query parameter;
extracting, by the entity extraction computer, one or more first entities from the partial search query parameters by comparing the partial search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more first entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more first entities in the partial search query parameters;

selecting, by a fuzzy-score matching computer, a fuzzy matching algorithm for searching the entity co-occurrence database to identify one or more records associated with the partial search query parameters, wherein the fuzzy matching algorithm corresponds to the at least one identified entity type;
searching, by the fuzzy-score matching computer, the entity co-occurrence database using the selected fuzzy matching algorithm and forming one or more first suggested search query parameters from the one or more records based on the search;
presenting, by the fuzzy-score matching computer, the one or more first suggested search query parameters via the user interface;
receiving by the entity extraction computer, user selection of the one or more first suggested search query parameters so as to form completed search query parameters;
extracting, by the entity extraction computer, one or more second entities from the completed search query parameters;
searching, by the entity extraction computer, the entity co-occurrence database to identify one or more entities related to the one or more second entities so as to form one or more second suggested search query parameters; and presenting, by the entity extraction computer, the one or more second suggested search query parameters via the user interface.

38. The method of claim 37 further comprising searching, by the fuzzy-score matching computer, the entity co-occurrence database using the selected fuzzy matching algorithm before the user input is finalized.

39. The method of claim 37 wherein the one or more records associated with the partial search query parameters include conceptual features.

40. The method of claim 37 wherein the one or more first suggested search query parameters include a plurality of first suggested search query parameters, the method further comprising sorting, by the fuzzy-score matching computer, the plurality of first suggested search query parameters in descending order based on proximity of a match to the partial search query parameters in the user input.

41. The method of claim 40 wherein the fuzzy-score matching computer presents the sorted plurality of first suggested search query parameters in a drop down list via the user interface.

42. The method of claim 37 wherein the entity co-occurrence database is indexed.

43. The method of claim 37 wherein the entity co-occurrence database includes an entity to entity index.

44. The method of claim 37 wherein the entity co-occurrence database includes an entity to topics index.

45. The method of claim 37 wherein the entity co-occurrence database includes an entity to facts index.

46. A system comprising:
one or more server computers having one or more processors executing computer readable instructions for a plurality of computer modules including:
an entity extraction module configured to receive user input of partial search query parameters from a user interface, the partial search query parameters having at least one incomplete search query parameter, the entity extraction module being further configured to:
extract one or more first entities from the partial search query parameters by comparing the partial search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more first entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more first entities in the partial search query parameters; and a fuzzy-score matching module configured to select a fuzzy matching algorithm for searching the entity co-occurrence database to identify one or more records associated with the partial search query parameters, wherein the fuzzy matching algorithm corresponds to the at least one identified entity type, the fuzzy-score matching module being further configured to:
search the entity co-occurrence database using the selected fuzzy matching algorithm and form one or more first suggested search query parameters from the one or more records based on the search, and present the one or more first suggested search query parameters via the user interface;
wherein the entity extraction module is further configured to:
receive user selection of the one or more first suggested search query parameters so as to form completed search query parameters, extract one or more second entities from the completed search query parameters, search the entity co-occurrence database to identify one or more entities related to the one or more second entities so as to form one or more second suggested search query parameters, and present the one or more second suggested search query parameters via the user interface.

47. The system of claim 46 wherein the fuzzy-score matching module is further configured to search the entity co-occurrence database using the selected fuzzy matching algorithm before the user input is finalized.

48. The system of claim 46 wherein the one or more records associated with the partial search query parameters include conceptual features.

49. The system of claim 46 wherein the one or more first suggested search query parameters include a plurality of first suggested search query parameters, the fuzzy-score matching module being further configured to sort the plurality of first suggested search query parameters in descending order based on proximity of a match to the partial search query parameters in the user input.

50. The system of claim 49 wherein the fuzzy-score matching computer is configured to present the sorted plurality of first suggested search query parameters in a drop down list via the user interface.

51. The system of claim 46 wherein the entity co-occurrence database is indexed.

52. The system of claim 46 wherein the entity co-occurrence database includes an entity to entity index.

53. The system of claim 46 wherein the entity co-occurrence database includes an entity to topics index.

54. The system of claim 46 wherein the entity co-occurrence database includes an entity to facts index.

55. A computer-implemented method comprising:

receiving, by a computer, from a search engine a search query comprising one or more strings of data, wherein each respective entity corresponds to a subset of the one or more strings;
identifying, by the computer, one or more entities in the one or more strings of data based on comparing the one or more entities against an entity database and a trends database;
identifying, by the computer, one or more features in the one or more strings of data not identified as corresponding to at least one entity;
assigning, by the computer, each of the one or more features to at least one of the one or more entities based on a matching algorithm;
assigning, by the computer, an extraction score to each respective entity based on a score assigned to each respective feature assigned to the respective entity;
receiving, by the computer, from an entity database a first search list containing one or more entities having a score within a threshold distance from the extraction score of each respective entity;
receiving, by the computer, from a trends database a second search list containing one or more entities having a score within a threshold distance from the extraction score of each respective entity;
generating, by the computer, an aggregated list comprising the first search list and the second search list, wherein the entities of the aggregated list are ranked according to the score of each respective aggregated list; and providing, by the computer, a suggested search according to the aggregated list.

56. A computer-implemented method comprises:
receiving, by a computer, a plurality data streams associated with a plurality of data sources respectively;
generating, by the computer, an array of properties associated with each of the respective data streams;
responsive to the computer detecting a triggering condition associated with the data of a data stream:
generating, by the computer, geographic data associated with the data of the data stream;
responsive to the computer not detecting the triggering condition for a data source:
mapping, by the computer, the array of properties for the data source to a set of managed properties associated with a search index; and responsive to determining a type of content of a data source is image data:
executing, by the computer, an optical character recognition routine on metadata associated with the data received from the data source; and retrieving, by the computer, from a web service identified by the metadata an updated data stream from the data source, wherein the data source is associated with the web service identified by the metadata.