Knowledge-based data mining system

Abstract

In a data mining system, data is gathered into a data store using, e.g., a Web crawler. The data is classified into entities. Data miners use rules to process the entities and append respective keys to the entities representing characteristics of the entities as derived from rules embodied in the miners. With these keys, characteristics of entities as defined by disparate expert authors of the data miners are identified for use in responding to complex data requests from customers.

1 .- 30 . (canceled) 
     
     
         31 . A computer-implemented method for collecting and associating unstructured data, comprising:
 receiving a plurality of documents, the documents comprising entities of interest;   analyzing the plurality of documents to identify the entities of interest, wherein a direct linkage does not exist between at least a first and a second identified entity of interest; and   determining from the plurality of documents a bridging entity, wherein:
 a first direct linkage exists between the first identified entity of interest and the bridging entity; and 
 a second direct linkage exists between the second identified entity of interest and the bridging entity. 
   
     
     
         32 . The computer-implemented method of  claim 31 , further comprising determining a third direct linkage between a third identified entity of interest and the bridging entity. 
     
     
         33 . The computer-implemented method of  claim 31 , further comprising finding documents other than the plurality of documents comprising the third identified entity of interest. 
     
     
         34 . The computer-implemented method of  claim 31 , further comprising creating a dossier for the first identified entity of interest, wherein the dossier comprises direct linkages and corresponding entities for the first identified entity of interest. 
     
     
         35 . The computer-implemented method of  claim 34 , further comprising:
 periodically determining if another bridging entity exists for the first identified entity of interest; and   updating the dossier if determined another bridging entity exists.   
     
     
         36 . The computer-implemented method of  claim 31 , wherein entities comprise at least one of the group consisting of:
 people,   dates,   companies,   institutions,   publications,   executed instruments,   events, and   locations.   
     
     
         37 . The computer-implemented method of  claim 31 , further comprising periodically determining if another bridging entity exists. 
     
     
         38 . A system for automatically collecting and associating unstructured data, comprising:
 a storage configured to receive a plurality of documents, the documents comprising entities of interest;   a data miner configured to:
 analyze the plurality of documents to identify the entities of interest, wherein a direct linkage does not exist between at least a first and a second identified entity of interest; and 
 determine from the plurality of documents a bridging entity, wherein:
 a first direct linkage exists between the first identified entity of interest and the bridging entity; and 
 a second direct linkage exists between the second identified entity of interest and the bridging entity. 
 
   
     
     
         39 . The system of  claim 38 , wherein the data miner is further configured to determine a third direct linkage between a third identified entity of interest and the bridging entity. 
     
     
         40 . The system of  claim 38 , further comprising an aggregator configured to:
 find documents other than the plurality of documents comprising the third identified entity of interest; and   send the found documents to the storage.   
     
     
         41 . The system of  claim 38 , further comprising a creation module to create a dossier for the first identified entity of interest, wherein the dossier comprises direct linkages and corresponding entities for the first identified entity of interest. 
     
     
         42 . The system of  claim 41 , wherein:
 the data miner is further configured to periodically determine if another bridging entity exists for the first identified entity of interest; and   the creation module is further configured to update the dossier if determined another bridging entity exists.   
     
     
         43 . The system of  claim 38 , wherein entities comprise at least one of the group consisting of:
 people,   dates,   companies,   institutions,   publications,   executed instruments,   events, and   locations.   
     
     
         44 . The system of  claim 38 , wherein the data miner is further configured to periodically determine if another bridging entity exists. 
     
     
         45 . A computer-implemented method for analyzing buzz regarding a product or service, comprising:
 receiving a plurality of documents;   in the plurality of documents, identifying locations where a product or service of interest is mentioned based on a determined context of the mention of the product or service of interest; and   determining from the mentions a buzz associated with the product or service of interest.   
     
     
         46 . The computer-implemented method of  claim 45 , further comprising:
 determining a buzz associated with a competing product or service; and   comparing the buzz associated with the product or service of interest to the buzz associated with the competing product or service of interest.   
     
     
         47 . The computer-implemented method of  claim 45 , further comprising:
 determining the buzz associated with the product or service of interest periodically over time; and   tracking trends over time in buzz associated with the product or service of interest.   
     
     
         48 . The computer-implemented method of  claim 47 , further comprising:
 identifying any trends to be alerted to a user; and   for any trends to be alerted, alerting the user of the trends.   
     
     
         49 . The computer-implemented method of  claim 47 , further comprising determining an effect of marketing activities for the product or service of interest on the trends. 
     
     
         50 . The computer-implemented method of  claim 49 , further comprising:
 segmenting the buzz based on at least one of geography or demographics;   segmenting the marketing activities based on at least one of geography or demographics; and   determining a correlation between the segmented marketing activities and the segmented buzz.   
     
     
         51 . The computer-implemented method of  claim 50 , wherein marketing activities comprises advertisement spending. 
     
     
         52 . The computer-implemented method of  claim 49 , wherein marketing activities comprises advertisement spending. 
     
     
         53 . A system for analyzing buzz regarding a product or service, comprising:
 a receiver to receive a plurality of documents;   a data miner to:
 in the plurality of documents locations, identify where a product or service of interest is mentioned based on a determined context of the mention of the product or service of interest; 
 determine a buzz associated with the product or service of interest periodically over time; and 
 determine trend over time in buzz associated with the product or service of interest. 
   
     
     
         54 . The system of  claim 53 , the data miner further to determine an effect of marketing activities for the product or service of interest on the trend. 
     
     
         55 . The system of  claim 53 , the data miner further to:
 segment the buzz based on at least one of geography or demographics;   receive information regarding marketing activities for the product or service of interest;   segment the marketing activity information based on at least one of geography or demographics; and   determine a correlation between the segmented marketing activity information and the segmented buzz.