基于知识图谱的文本观点检索方法

doi:10.6040/j.issn.1671-9352.0.2016.250

山东大学学报（理学版） ›› 2016, Vol. 51 ›› Issue (11): 33-40.doi: 10.6040/j.issn.1671-9352.0.2016.250

基于知识图谱的文本观点检索方法

马飞翔^1,2,廖祥文^1,2*,於志勇^1,2,吴运兵^1,2,陈国龙^1,2

1.福州大学数学与计算机科学学院, 福建福州 350116;2.福建省网络计算与智能信息处理重点实验室(福州大学), 福建福州 350116

收稿日期:2016-06-07 出版日期:2016-11-20 发布日期:2016-11-22
通讯作者: 廖祥文(1980— ),男,博士,副教授,研究方向为文本倾向性检索与挖掘. E-mail:liaoxw@fzu.edu.cn E-mail:457429397@qq.com
作者简介:马飞翔(1991— ),男,硕士研究生,研究方向为知识图谱与观点检索. E-mail:457429397@qq.com
基金资助:
国家自然科学基金青年项目(61300105);教育部博士点基金联合资助项目(2012351410010);福建省科技重大专项项目(2013H6012);中国科学院网络数据科学与技术重点实验室开放基金课题项目(CASNDST20140X)

A text opinion retrieval method based on knowledge graph

MA Fei-xiang^1,2, LIAO Xiang-wen^1,2*, YU Zhi-yong^1,2, WU Yun-bing^1,2, CHEN Guo-long^1,2

1. School of Math and Computer Science, Fuzhou University, Fuzhou 350116, Fujian, China;
2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing(Fuzhou University), Fuzhou 350116, Fujian, China

Received:2016-06-07 Online:2016-11-20 Published:2016-11-22

摘要/Abstract

摘要： 文本观点检索旨在检索出与查询主题相关并且表达用户对主题观点的文档。由于用户查询时输入通常很短,难以准确表示查询的信息需求。知识图谱是结构化的语义知识库,通过知识图谱中的知识有助于理解用户的信息需求。因此,提出了一种基于知识图谱的文本观点检索方法。首先由知识图谱获取候选查询扩展词,并计算每个候选词扩展词分布、共现频率、邻近关系、文档集频率,然后利用4类特征通过SVM分类得到扩展词,最后利用扩展词对产生式观点检索模型进行扩展,实现对查询的观点检索。实验表明,在微博和推特两个数据集上,与基准工作对比,所提出的方法在MAP、NDCG等评价指标上均有显著的提升。

关键词: 知识图谱, 观点检索, 查询扩展

Abstract: Text opinion retrieval aims at finding relevant and opinionate documents according to a users query. User queries are usually too short to describe the information need accurately. Knowledge Graph is a structured semantic knowledge base, which the information of knowledge graph can help us to describe the information need. In this paper, we propose a text opinion retrieval method based on knowledge graph. Firstly, we get the candidate of query expansion terms by knowledge graph, and calculate four kinds of features of each candidate named term distributions, co-occurrence frequency, proximity and collection frequency. Then, we choose the expansion terms by SVM classifier with the features. Finally, we expand the generative opinion retrieval model using the expansion terms to get the opinion retrieval result. Experimental results on Sina Microblog and Twitter datasets show that our proposed method obtains significant improvements in terms of MAP and NDCG over the baseline approaches.

Key words: opinion retrieval, query expansion, knowledge graph

中图分类号:

TP391

马飞翔,廖祥文,於志勇,吴运兵,陈国龙. 基于知识图谱的文本观点检索方法[J]. 山东大学学报（理学版）, 2016, 51(11): 33-40.

MA Fei-xiang, LIAO Xiang-wen, YU Zhi-yong, WU Yun-bing, CHEN Guo-long. A text opinion retrieval method based on knowledge graph[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(11): 33-40.

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基于知识图谱的文本观点检索方法

A text opinion retrieval method based on knowledge graph

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