A ranking method for academic search engines based on community analysis

Abstract

The invention discloses an academic search engine sorting method based on community analysis. In the academic search engine, a two-dimensional complex graph model is established based on the work citation relationship and author cooperation relationship within the academic circle, and converted into a one-dimensional graph model. , using the weighted tag propagation method for community analysis, dividing the work information into different communities, and then mapping the community relationship on the basis of the user input to search content, and then through the sorting strategy based on the random walk process, The content within the community is sorted with reference to the text similarity and the number of walks of the graph nodes, and finally the collection of works required by the user is obtained. The method of the present invention can find hidden related content that cannot be found by the traditional sorting method of academic search engines, overcomes the shortcomings of traditional methods that rely too much on text similarity, and at the same time, the operation of the method requires less time, and is suitable for sorting large-scale academic search engines Scenes.

Description

technical field [0001] The invention relates to information retrieval and complex networks, in particular to a method for sorting content by search engines and performing community analysis in complex networks formed by academic circles, and in particular to a method for sorting academic search engines based on community analysis. Background technique [0002] With the rapid development of the Internet, Web services based on the HTTP protocol are becoming more and more popular, and the amount of resources and information on the Internet has increased dramatically. Users have the need to find resources distributed in various locations on the Internet based on their own personalized information. In July 1994, Lycos launched the data mining technology based on the robot protocol to support the sorting of search results, which is an important progress in the history of search engines. In 1995, the meta search engine was born. After a user submits a search request, the meta-sear...

AI Technical Summary

Problems solved by technology

[0005] Although traditional academic search engines such as Google Scholar, Microsoft Academic Search, Baidu Academic Search, etc. have a relatively complete function of matching and finding relevant content in the document index library according to the text content, because the index is mainly based on the text similarity of the retrieved content Therefore, for some documents that are closely related to the search content but do not directly have a high text similarity, they are ignored in this mechanism with a high probability, resulting in incomplete and inaccurate search results.

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