JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (11): 33-40.doi: 10.6040/j.issn.1671-9352.0.2016.250

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A text opinion retrieval method based on knowledge graph

MA Fei-xiang1,2, LIAO Xiang-wen1,2*, YU Zhi-yong1,2, WU Yun-bing1,2, CHEN Guo-long1,2   

  1. 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

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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

CLC Number: 

  • TP391
[1] OUNIS I, DE RIJKE M, MACDONALD C, et al. Overview of the TREC-2006 blog track [J]. DTIC Document, 2006.
[2] ZHANG W, YU C, MENG W. Opinion retrieval from blogs[C] // Proceedings of the 16th ACM Conference on Information and Knowledge Management. New York: ACM, 2007: 831-840.
[3] HE B, MACDONALD C, HE J, et al. An effective statistical approach to blog post opinion retrieval[C] // Proceedings of the 17th ACM Conference on Information and Knowledge Management. New York: ACM, 2008: 1063-1072.
[4] GERANI S, CARMAN M J, CRESTANI F. Proximity-based opinion retrieval[C] // Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2010: 403-410.
[5] LUO Z, OSBORNE M, WANG T. An effective approach to tweets opinion retrieval [J]. World Wide Web, 2015, 18(3):545-566.
[6] ZHANG M, YE X. A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval[C] // Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2008: 411-418.
[7] 刘峤, 李杨, 段宏 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3):582-600. LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53(3):582-600.
[8] BOLLACKER K, EVANS C, PARITOSH P, et al. Freebase: a collaboratively created graph database for structuring human knowledge[C] // Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2008: 1247-1250.
[9] HOFFART J, SUCHANEK F M, BERBERICH K, et al. YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia[J]. Artificial Intelligence, 2013, 194(28-61).
[10] DONG X, GABRILOVICH E, HEITZ G, et al. Knowledge vault: a web-scale approach to probabilistic knowledge fusion[C] // Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 601-610.
[11] VRANDECIC D, KR TZSCH M. Wikidata: a free collaborative knowledgebase[J]. Communications of the ACM, 2014, 57(10):78-85.
[12] DALTON J, DIETZ L, ALLAN J. Entity query feature expansion using knowledge base links[C] // Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. New York: ACM, 2014: 365-374.
[13] XIONG C, CALLAN J. Query expansion with Freebase[C] // Proceedings of the 2015 International Conference on the Theory of Information Retrieval. New York: ACM, 2015: 111-120.
[14] LI B, ZHOU L, FENG S, et al. A unified graph model for sentence-based opinion retrieval[C] // Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Somerset: Association for Computational Linguistics, 2010: 1367-1375.
[15] LIAO X-W, CHEN H, WEI J-J, et al. A weighted lexicon-based generative model for opinion retrieval[C] // 2014 International Conference on Machine Learning and Cybernetics(ICMLC). Washington: IEEE Computer Society, 2014:821-826.
[16] CAO G, NIE J-Y, GAO J, et al. Selecting good expansion terms for pseudo-relevance feedback[C] // Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2008: 243-250.
[17] ESULI A, SEBASTIANI F. Determining the semantic orientation of terms through gloss classification[C] // Proceedings of the 14th ACM International Conference on Information and Knowledge Management. New York: ACM, 2005: 617-624.
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