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现代图书情报技术  2016, Vol. 32 Issue (6): 1-11     https://doi.org/10.11925/infotech.1003-3513.2016.06.01
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于多知识库的短文本实体链接方法研究*——以Wikipedia和Freebase为例
周鹏程1(),武川1,陆伟1,2
1武汉大学信息管理学院 武汉 430072
2武汉大学信息资源研究中心 武汉 430072
Entity Linking Method for Short Texts with Multi-Knowledge Bases: Case Study of Wikipedia and Freebase
Zhou Pengcheng1(),Wu Chuan1,Lu Wei1,2
1School of Information Management, Wuhan University, Wuhan 430072, China
2Center for the Studies of Information Resources, Wuhan University, Wuhan 430072, China
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摘要 

目的】基于多知识库进行实体链接, 解决基于单一知识库的实体链接覆盖度低的问题。【方法】首先生成文本的n-gram并利用词性和多个指称-实体字典获取候选指称, 然后生成指称组合并保留覆盖度最大且不被其他组合包含的指称组合, 接着生成候选实体序列并利用多知识库信息计算实体序列的相关度, 最后选择相关度最大的实体序列为最终结果。【结果】以Wikipedia和Freebase为例的实验结果表明, 基于Wikipedia+Freebase的实体链接准确率、召回率、F值分别达到71.81%、76.86%、74.25%。【局限】基于词性过滤n-gram缺乏理论依据, 数据集FACC1具有高准确率和低召回率的特点。【结论】利用多个知识库的实体信息, 能够提升实体链接效果。

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周鹏程
武川
陆伟
关键词 实体链接知识库WikipediaFreebase    
Abstract

[Objective] This paper proposes an entity linking method using multi-knowledge bases, aiming at solving the problem of low coverage caused by entity linking with single knowledge base. [Methods] First, we generated n-gram of input text and obtained candidate mentions using part of speech and multi-mention-entity dictionary. Second, we generated and retained mention combinations of highest coverage which are not contained by other mention combinations. Third, we generated entity sequences and calculated their relevence degree using information from multi-knowledge bases. We listed entity sequence with the highest relevence degree as the final result. [Results] This case study showed that the Precision, Recall, and F-value of the entity linking based on Wikipedia+Freebase reaches 71.81%, 76.86%, and 74.25% respectively. [Limitations] Filtering n-gram based on part of speech lacked theoretical foundation, and the FACC1 dataset featured high precision but low recall. [Conclusions] Utilizing entity information from multi-knowledge bases can improve the performance of entity linking.

Key wordsEntity linking    Knowledge base    Wikipedia    Freebase
收稿日期: 2016-01-13      出版日期: 2016-07-18
基金资助:*本文系国家自然科学基金面上项目“基于语言模型的通用实体检索建模及框架实现研究”(项目编号: 71173164)和武汉大学与中国科技信息研究所合作项目“科学文献的语义功能识别与深度利用”的研究成果之一
引用本文:   
周鹏程,武川,陆伟. 基于多知识库的短文本实体链接方法研究*——以Wikipedia和Freebase为例[J]. 现代图书情报技术, 2016, 32(6): 1-11.
Zhou Pengcheng,Wu Chuan,Lu Wei. Entity Linking Method for Short Texts with Multi-Knowledge Bases: Case Study of Wikipedia and Freebase. New Technology of Library and Information Service, 2016, 32(6): 1-11.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2016.06.01      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2016/V32/I6/1
[1] Zhang W, Sim Y C, Su J, et al.Entity Linking with Effective Acronym Expansion, Instance Selection and Topic Modeling [C]. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain. 2011: 1909-1914.
[2] Pantel P, Fuxman A.Jigs and Lures: Associating Web Queries with Structured Entities [C]. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA. 2011: 83-92.
[3] Lin T, Etzioni O.Entity Linking at Web Scale [C]. In: Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, Montreal, Canada. 2012: 84-88.
[4] Welty C, Murdock J W, Kalyanpur A, et al.A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson [C]. In: Proceedings of the 11th International Conference on the Semantic Web. Springer-Verlag, 2012: 243-256.
[5] Bollacker K, Evans C, Paritosh P, et al.Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge [C]. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. ACM, 2008: 1247-1250.
[6] Suchanek F M, Kasneci G, Weikum G.YAGO: A Core of Semantic Knowledge [C]. In: Proceedings of the 16th International Conference on World Wide Web. ACM, 2007: 697-706.
[7] Auer S, Bizer C, Kobilarov G, et al.DBpedia: A Nucleus for a Web of Open Data [C]. In: Proceedings of the 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, Busan, Korea. 2007: 722-735.
[8] ClueWeb09 Related Data: Freebase Annotations of the ClueWeb Corpora, v1 (FACC1) [EB/OL]. (2013-11-04). [2015-11-24]. .
[9] Brand?o W C, Santos R L T, Ziviani N, et al. Learning to Expand Queries Using Entities[J]. Journal of the Association for Information Science and Technology, 2014, 65(9): 1870-1883.
[10] 陆伟, 武川. 实体链接研究综述[J]. 情报学报, 2015, 34(1): 105-112.
[10] (Lu Wei, Wu Chuan.Literature Review on Entity Linking[J]. Journal of the China Society for Scientific and Technical Information, 2015, 34(1): 105-112.)
[11] Cucerzan S.Large-scale Named Entity Disambiguation Based on Wikipedia Data [C]. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2007: 708-716.
[12] Milne D, Witten I H.Learning to Link with Wikipedia [C]. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. ACM, 2008: 509-518.
[13] Ferragina P, Scaiella U.Tagme: On-the-fly Annotation of Short Text Fragments (by Wikipedia Entities) [C]. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, Ontario, Canada. 2010: 1625-1628.
[14] Meij E, Weerkamp W, De Rijke M.Adding Semantics to Microblog Posts [C]. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining. ACM, 2012: 563-572.
[15] Sil A, Yates A.Re-ranking for Joint Named-entity Recognition and Linking [C]. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. ACM, 2013: 2369-2374.
[16] Mihalcea R, Csomai A.Wikify!: Linking Documents to Encyclopedic Knowledge [C]. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management, Lisboa, Portugal. 2007: 233-242.
[17] Zhang W, Su J, Tan C L, et al.Entity Linking Leveraging: Automatically Generated Annotation [C]. In: Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics, Beijing, China. 2010: 1290-1298.
[18] Pilz A, Paa? G.From Names to Entities Using Thematic Context Distance [C]. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, Scotland, UK. 2011: 857-866.
[19] Zheng Z, Li F, Huang M, et al.Learning to Link Entities with Knowledge Base [C]. In: Proceedings of Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2010: 483-491.
[20] Ratinov L, Roth D, Downey D, et al.Local and Global Algorithms for Disambiguation to Wikipedia [C]. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2011: 1375-1384.
[21] Shen W, Wang J, Luo P, et al.LINDEN: Linking Named Entities with Knowledge Base via Semantic Knowledge [C]. In: Proceedings of the 21st International Conference on World Wide Web, Lyon, France. 2012: 449-458.
[22] Han X, Sun L, Zhao J.Collective Entity Linking in Web Text: A Graph-based Method [C]. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China. 2011: 765-774.
[23] Hoffart J, Yosef M A, Bordino I, et al.Robust Disambiguation of Named Entities in Text [C]. In: Proceedingsof the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011: 782-792.
[24] Hachey B, Radford W, Curran J.Graph-Based Named Entity Linking with Wikipedia [C]. In: Proceedings of the 12th International Conference on Web Information System Engineering. 2011: 213-226.
[25] Guo Y, Che W, Liu T, et al.A Graph-based Method for Entity Linking [C]. In: Proceedings of the 5th International Joint Conferenceon Natural Language Processing, Chiang Mai, Thailand. 2011: 1010-1018.
[26] Gottipati S, Jiang J.Linking Entities to a Knowledge Base with Query Expansion [C]. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011: 804-813.
[27] Zhang W, Sim Y C, Su J, et al.NUS-I2R: Learning a Combined System for Entity Linking [C]. In: Proceedings of Text Analysis Conference 2010 Workshop, Gaithersburg, Maryland, USA. 2010.
[28] Chen Z, Ji H.Collaborative Ranking: A Case Study on Entity Linking [C]. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Scotland, UK. 2011: 771-781.
[29] Liu X, Li Y, Wu H, et al.Entity Linking for Tweets [C]. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2013.
[30] Wu C, Lu W, Zhou P.An Optimization Framework for Entity Recognition and Disambiguation [C]. In: Proceedings of the 1st International Workshop on Entity Recognition & Disambiguation. ACM, 2014: 105-110.
[31] Bunescu R C, Pasca M.Using Encyclopedic Knowledge for Named Entity Disambiguation [C]. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy. 2006: 9-16.
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