Frontiers of Data and Domputing ›› 2022, Vol. 4 ›› Issue (2): 50-62.

doi: 10.11871/jfdc.issn.2096-742X.2022.02.005

• Special Issue: Advanced Intelliget Computing Platform and Application • Previous Articles     Next Articles

Educational Resource Search Strategy Based on ElasticSearch and Semantic Similarity Matching

TAO Lei(),SU Chenyang(),LI Zhengdan*(),ZHU Jingwen(),ZHANG Yuzhi()   

  1. Department of Software, Nankai University, Tianjin 300450, China
  • Received:2022-02-06 Online:2022-04-20 Published:2022-04-30
  • Contact: LI Zhengdan E-mail:stonebegin@sina.com;15731471310@163.com;lzd@nankai.edu.cn;zhujingwen@nankai.edu.cn;zyz@nankai.edu.cn

Abstract:

[Objective] In order to integrate a variety of educational resources and help users obtain rich education contents, this paper presents the design and implementation of an efficient and accurate search strategy under this scenario. [Context] There are many types of educational resources and their quantity is huge. Users' demand for accurate retrieval is growing day by day. The effect of the current search approach based on ElasticSearch is not satisfactory. [Methods] After preprocessing and word segmentation of the query input by the user, n approximate results are matched in the query database through the ER-BERT semantic similarity model and inputted into ElasticSearch, and then the correlation calculation formula are constructed. Finally, the matching results are sorted according to the final score of the comprehensive evaluation. [Results] Using knowledge graph technology to integrate complex education resources, an education resource search strategy based on ElasticSearch and semantic similarity matching is realized. While ensuring the retrieval speed, it can be used to search according to the semantic information of the query retrieved by users. [Conclusions] The experiment results show that using this education resource search strategy increases the number of search results, improves the accuracy of results while ensuring the search speed, and significantly improves the user's search experience.

Key words: ElasticSearch, text similarity, search strategy, knowledge graph