Search suggestions using fuzzy-score matching and entity co-occurrence

Abstract

A method for generating search suggestions by using fuzzy-score matching and entity co-occurrence in a knowledge base is disclosed. Embodiments of the method may be employed in any search system that may include an entity extraction computer module that may perform partial entity extractions from provided search queries, a fuzzy-score matching computer module that may generate algorithms based on the type of entity extracted and perform a search against an entity co-occurrence knowledge base. The entity co-occurrence knowledge base, which may include 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. The suggestions may include alternates to the partial query provided by the user that may enhance and save time when performing searches.

What is claimed is: 
     
         1 . A method comprising:
 in response to comparing, by a server, a search query against a plurality of records of an in-memory database, identifying, by the server, an entity corresponding to the search query and a type of the entity corresponding to the search query, wherein the search query is received from a client, wherein the records contain a co-occurrence knowledge dataset; and   in response to selecting, by the server, a fuzzy-score matching algorithm based on the type, determining, by the server, that the entity is sufficiently similar to a record of the records based on the fuzzy-score matching algorithm and presenting, by the server, a content of the record to the client, wherein the fuzzy-score matching algorithm uses a metric having a minimum number of single-character edits that are needed to change a first word in the search query into a second word in the record.   
     
     
         2 . The method of  claim 1 , wherein the entity corresponds to at least one of a person content, an organization content, a location of a place content, or a date content. 
     
     
         3 . The method of  claim 1 , wherein the in-memory database is indexed as structured data. 
     
     
         4 . The method of  claim 3 , wherein the structured data includes at least one of entity-to-entity index, an entity-to-topic index, or an entity-to-fact index. 
     
     
         5 . The method of  claim 1 , further comprising:
 selecting, by the server, the record as a first candidate to be presented on the client as a search suggestion.   
     
     
         6 . The method of  claim 5 , further comprising:
 presenting, by the server, at least some of the records other than the record to the client, positionally under the record in the search suggestion, in a descending order.   
     
     
         7 . The method of  claim 1 , wherein the metric employs a Levenshtein distance. 
     
     
         8 . The method of  claim 1 , wherein the presenting of the content of the record is before the search query is finalized at the client. 
     
     
         9 . The method of  claim 1 , wherein the in-memory database includes a search controller, a search node, and a collection of compressed data. 
     
     
         10 . The method of  claim 1 , wherein the comparing, the identifying, the selecting, and the determining are in real-time as the search query is being received from the client. 
     
     
         11 . A system comprising:
 a server programmed to:
 responsive to comparing, by the server, a search query against a plurality of records of an in-memory database, identify, by the server, an entity corresponding to the search query and a type of the entity corresponding to the search query, wherein the search query is received from a client, wherein the records contain a co-occurrence knowledge dataset; and 
 responsive to selecting, by the server, a fuzzy-score matching algorithm based on the type of the entity, determine, by the server, that the entity is sufficiently similar to a record of the records based on the fuzzy-score matching algorithm and present, by the server, a content of the record to the client, wherein the fuzzy-score matching algorithm uses a metric having a minimum number of single-character edits that are needed to change a first word in the search query into a second word in the record. 
   
     
     
         12 . The server of  claim 11 , wherein the entity corresponds to at least one of a person content, an organization content, a location of a place content, or a date content. 
     
     
         13 . The server of  claim 11 , wherein the in-memory database is indexed as structured data. 
     
     
         14 . The server of  claim 13 , wherein the structured data includes at least one of entity-to-entity index, an entity-to-topic index, or an entity-to-fact index. 
     
     
         15 . The server of  claim 11 , wherein the server is programmed to select the record as a first candidate to be presented on the client as a search suggestion. 
     
     
         16 . The server of  claim 11 , wherein the server is programmed to present at least some of the records other than the record to the client, positionally under the record in the search suggestion, in a descending order. 
     
     
         17 . The server of  claim 11 , wherein the metric employs a Levenshtein distance. 
     
     
         18 . The server of  claim 11 , wherein the server is programmed to present the content of the record before the search query is finalized at the client. 
     
     
         19 . The server of  claim 11 , where the in-memory database includes a search controller, a search node, and a collection of compressed data. 
     
     
         20 . The server of  claim 11 , wherein the server is programmed to compare, identify, select and determine in real-time as the search query is being received from the client.