面向知识库问答的实体链接是指将自然语言问句中实体指称链接到知识库中实体的方法。目前主要面临两个问题: 第一是自然语言问句短,实体指称上下文不充分;第二是结构化知识库中实体的文本描述信息少。因此,该文提出了分别利用候选实体的类别、关系和邻近实体作为候选实体表示的方法,弥补知识库实体描述信息不足的问题。同时,通过语料训练得到问句指称的相似实体指称作为其背景知识。最后,结合实体流行度,共同作为实体消歧的特征。实验结果表明,上述提到所有特征的线性组合在数据集上高于单个特征的结果,表现最佳。
Abstract
Entity linking for knowledge base question answering is to link the entity mention in the natural language question to a target entity in the knowledge base. This paper employs the candidate entity's types, relationships and neighboring entities as the candidate entity representation, so as to solve the problem of insufficient description information of the entity in the knowledge base. At the same time, the similar entity mentions obtained by training the corpus are considered as the mention's background knowledge. Finally, the proposed features combine the entity popularity feature to solve the entity disambiguation problem. The experimental results on the data set show that the linear combination of all the above-mentioned features is better than the single feature.
关键词
知识库问答 /
实体链接 /
实体消歧 /
Freebase
{{custom_keyword}} /
Key words
knowledge base question answering /
entity linking /
entity disambiguation /
Freebase
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Bollacker K, Cook R, Tufts P. Freebase: A shared database of structured general human knowledge[C]//Proceedings of the AAAI,2007: 1962-1963.
[2] Bizer C, Lehmann J, Kobilarov G, et al. DBpedia-a crystallization point for the web of data[J].Web Semantics: Science, Services and Agents on the World Wide Web, 2009, 7(3): 154-165.
[3] Fabian M S, Gjergji K, Gerhard W. Yago: A core of semantic knowledge unifying wordnet and wikipedia[C]//Proceedings of the 16th International World Wide Web Conference, WWW,2007: 697-706.
[4] Niu X, Sun X, Wang H, et al. Zhishi. Me-weaving Chinese linking open data[C]//Proceedings of the International Semantic Web Conference. Springer, Berlin, Heidelberg, 2011: 205-220.
[5] Xu B, Xu Y, Liang J, et al. CN-DBpedia: A never-ending Chinese knowledge extraction system[C]//Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, Cham, 2017: 428-438.
[6] City T. Wikipedia the free encyclopedia[EB/OL]. [2016-03-12].http://en.wikipedia.org/wiki/Think_City.
[7] Zheng Z, Si X, Li F, et al. Entity disambiguation with freebase[C]//Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01. IEEE Computer Society, 2012: 82-89.
[8] Guo S, Chang M W, Kiciman E. To link or not to link? A study on end-to-end tweet entity linking[C]//Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2013: 1020-1030.
[9] Tan C, Wei F, Ren P, et al. Entity linking for queries by searching wikipedia sentences[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing,2017: 68-77.
[10] Varma V, Bharat V, Kovelamudi S, et al. IIIT Hyderabad at TAC 2009[C]//Proceedings of the Text Analysis Conference,2009: 620-622.
[11] Lehmann J, Monahan S, Nezda L, et al. LCC approaches to knowledge base population at TAC 2010[R].Maryland: NIST, 2010.
[12] Bunescu R,Pa?ca M.Using encyclopedic knowledge for named entity disambiguation[C]//Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics,2006:9-16.
[13] Mihalcea R,Csomai A.Wikify! linking documents to encyclopedic knowledge[C]//Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management.ACM,2007:233-242.
[14] Dredze M,Mcnamee P,Rao D,et al.Entity disambiguation for knowledge base population[C]//Proceedings of the International Conference on Computational Linguistics.Association for Computational Linguistics,2010:277-285.
[15] Han X,Sun L.A generative entity-mention model for linking entities with knowledge base[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies.Association for Computational Linguistics,2011:945-954.
[16] Barrena A,Soroa A,Agirre E.Alleviating poor context with background knowledge for named entity disambiguation[C]//Proceedings of the Meeting of the Association for Computational Linguistics,2016:1903-1912.
[17] Yang Y,Chang M W.S-MART:Novel tree-based structured learning algorithms applied to tweet entity linking[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing,2015:504-513.
[18] Cornolti M,Ferragina P,Ciaramita M.A piggyback system for joint entity mention detection and linking in web queries[C]//Proceedings of the International Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2016:567-578.
[19] Cao Y,Li J,Guo X,et al.Name list only? Target entity disambiguation in short texts[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,2015:654-664.
[20] Yih W,Richardson M,Meek C,et al.The value of semantic parse labeling for knowledge base question answering[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics,2016:201-206.
[21] Qu Y,Liu J,Kang L,et al.Question answering over freebase via attentive RNN with similarity matrix based CNN[J].arXiv preprint arXiv:1804.03317,2018.
[22] Peters M,Neumann M,Iyyer M,et al.Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,2018:2227-2237.
[23] Devlin J,Chang M W,Lee K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv preprint arXiv:1810.04805,2018.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金(61502095);江苏省自然科学基金(BK20140643)
{{custom_fund}}
PDF(2153 KB)
