System and method for topic meta search based on unsupervised entity relation extraction

Abstract

The invention discloses a system and a method for topic meta search based on unsupervised entity relation extraction. The system comprises a topic model building module which is used for building various topic models, a matching search engine module and a search result processing module, wherein the matching search engine module is used for matching suitable member search engines so as to conduct topic search in accordance with different topic models when a user selects topics which are required to be searched, and for search results returned by member search engines, the search result processing module is used for conducting entity relation extraction for characteristic words of search results by using the unsupervised entity relation extraction algorithm and for returning results meeting conditions to the user in accordance with the similarity of the extracted relation on calculation and topics. On the premise of the recall ratio, according to results of unsupervised relation extraction, the similarity of topics is determined and the high recall ratio is obtained.

Description

technical field [0001] The present invention relates to a subject meta search system and method, in particular to a subject meta search system and method based on unsupervised entity relationship extraction. Background technique [0002] Among the many current search services, there are some personalized information search services for different users, such as personalized search services based on user behavior analysis. To a certain extent, identify the differences in the individual information needs of different users. However, due to the inability to accurately determine and describe the user's query subject, or the extraction algorithm of the search result feature vector is different, the discrete feature vector cannot correctly express the search result document, thus affecting the accuracy of the search. Therefore, how to express the topic more accurately in the process of searching, and how to calculate the similarity between the search result and the topic more accu...

AI Technical Summary

Problems solved by technology

[0003] In many topic meta-search methods, the method of extracting feature vectors is generally used for search results, and then the angle cosine algorithm is used to calculate the degree of conformity between the search results and the topic, but the feature vectors are discrete, which may not be able to correctly express the search result documents , so the calculation of the similarity with the topic will not be accurate enough, and the accuracy of the search results will be greatly affected

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