计算机科学 ›› 2022, Vol. 49 ›› Issue (9): 202-207.doi: 10.11896/jsjkx.220300277
饶志双1, 贾真1, 张凡1,2, 李天瑞1,2,3
RAO Zhi-shuang1, JIA Zhen1, ZHANG Fan1,2, LI Tian-rui1,2,3
摘要： 基于知识图谱的问答(Question Answering over Knowledge Graph,KG-QA)系统通过对给定的自然语言问题进行语义解析,将问题映射到知识图谱〈主,谓,宾〉三元组,并对三元组进行推理得到问题的答案。由于自然语言具有多样性的特点,一个问题可能有多种表述,而三元组知识在知识图谱中却是规范的结构化数据,如何将自然语言问题映射到知识图谱三元组是KG-QA的难点。文中提出了一种新的Key-Value关联记忆网络,从知识图谱的角度出发,关注候选答案知识间的关联关系以及知识图谱中的知识与自然语言问题表征之间的关系。此外,在模型中引入了注意力机制,使其具有更好的可解释性。在WebQuestions数据集上进行实验,结果表明,所提方法的F1值比基于信息抽取的最优方法提高了5.9%,比基于语义分析的最优方法略有提高,验证了该方法的有效性。
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| [1]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.2008:1247-1250. [2]SUCHANEK F M,KASNECI G,WEIKUM G.Yago:a core of semantic knowledge[C]//Proceedings of the 16th International Conference on World Wide Web.2007:697-706. [3]YAO X,VAN DURME B.Information extraction over struc-tured data:question answering with freebase[C]//Proceedings of the 52nd Annual Meeting of the Association for ComputationalLinguistics(Volume 1:Long Papers).2014:956-966. [4]DONG L,WEI F,ZHOU M,et al.Question answering overfreebase with multi-column convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Volume 1:Long Papers).2015:260-269. [5]LECUN Y,BENGIO Y.Convolutional networks for images,speech,and time series[J].The Handbook of Brain Theory and Neural Networks,1995,3361(10):1995. [6]ZHANG S,FENG Y,HUANG S,et al.Semantic interpretation of superlative expressionsvia structured knowledge bases[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Volume 2:Short Papers).2015:225-230. [7]WESTON J,CHOPRA S,BORDES A.Memory Networks[J].arXiv:1410.3916,2015. [8]MILLER A,FISCH A,DODGE J,et al.Key-Value MemoryNetworks for Directly Reading Documents[J].arXiv:1606.03126,2016. [9]TUNSTALL-PEDOE W.True knowledge:Open-domain ques-tion answering using structuredknowledge and inference[J].AI Magazine,2010,31(3):80-92. [10]BERANT J,CHOU A,FROSTIG R,et al.Semantic parsing on freebase from question-answer pairs[C]//Proceedings of the 2013 International Conference on Empirical Methods in Natural Language Processing.2013:1533-1544. [11]REDDY S,LAPATA M,STEEDMAN M.Large-scale semantic parsing without question-answer pairs[J].Transactions of the Association for Computational Linguistics,MIT Press,2014,2:377-392. [12]HAKIMOV S,UNGER C,WALTER S,et al.Applying semantic parsing to question answering over linked data:addressing the lexical gap[C]//International Conference on Applications of Natural Language to Information Systems.2015:103-109. [13]UNGER C,BÜHMANN L,LEHMANN J,et al.Template-based question answering over RDF data[C]//Proceedings of the 21st International Conference on World Wide Web.2012:639-648. [14]DONG L,LAPATA M.Language to logical form with neural attention[J].arXiv:1601.01280,2016. [15]CUI W,XIAO Y,WANG H,et al.KBQA:learning question answering over QA corpora and knowledge bases[J].arXiv:1903.02419,2019. [16]BORDES A,CHOPRA S,WESTON J.Question answering withsubgraph embeddings[J].arXiv:1406.3676,2014. [17]BORDES A,WESTON J,USUNIER N.Open question answe-ring with weakly supervised embedding models[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.2014:165-180. [18]HE X,GOLUB D.Character-level question answering with at-tention[C]//Proceedings of the 2016 International Conference on Empirical Methods in Natural Language Processing.2016:1598-1607. [19]HUANG X,ZHANG J,LI D,et al.Knowledge graph embedding based question answering[C]//Proceedings of the Twelfth ACM International Conference on Web Search and Data Mi-ning.2019:105-113. [20]SAXENA A,TRIPATHI A,TALUKDAR P.Improving Multi-hop Question Answering over Knowledge Graphs using Know-ledge Base Embeddings[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Online:Association for Computational Linguistics,2020:4498-4507. [21]LU Q,PAN Z S,XIE J.Bidirectional Attention Question An-swering Model Combining Knowledge Representation Learning[J].Computer Engineering and Applications,2021,57(23):171-177. [22]LIU X L,HAN F,WANG Z J.Joint Question Answering Model Based on Knowledge Representation[J].Computer Science,2021,48(6):241-245. [23]BAO J,DUAN N,YANZ,et al.Constraint-based question answering with knowledge graph[C]//Proceedings of the 26th International Conference on Computational Linguistics:Technical Papers.2016:2503-2514. [24]BERANT J,LIANG P.Semantic parsing via paraphrasing[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2014:1415-1425. [25]BAST H,HAUSSMANN E.More accurate question answering on freebase[C]//Proceedings of the 24th ACM International on Conference on Informationand Knowledge Management.2015:1431-1440. [26]ABUJABAL A,YAHYA M,RIEDEWALD M,et al.Automated template generation for question answering over knowledge graphs[C]//Proceedings of the 26th International Conference on World Wide Web.2017:1191-1200. [27]HU S,ZOU L,YU J X,et al.Answering natural language questions by subgraph matching over knowledge graphs[J].IEEE Transactions on Knowledge and Data Engineering,2017,30(5):824-837. [28]YANG M C,DUAN N,ZHOU M,et al.Joint relational embeddings for knowledge-based question answering[C]//Proceedings of the 2014 International Conference on Empirical Methods in Natural Language Processing.2014:645-650. [29]YAMADA I,ASAI A,SAKUMA J,et al.Wikipedia2Vec:An efficient toolkit for learning and visualizing the embeddings of words and entities from Wikipedia[J].arXiv:1812.06280,2018. [30]BORDES A,USUNIER N,CHOPRA S,et al.Large-scale simple question answering with memory networks[J].arXiv:1506.02075,2015. [31]XU K,REDDY S,FENG Y,et al.Question answering on freebase via relation extraction and textual evidence[J].arXiv:1603.00957,2016. [32]HAO Y,ZHANG Y,LIU K,et al.An end-to-end model forquestion answering over knowledge base with cross-attention combining global knowledge[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2017:221-231. [33]XU K,LAI Y,FENG Y,et al.Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering[C]//Proceedings of the 2019 Conference of the North.Minneapolis,Minnesota:Association for Computational Linguistics,2019:2937-2947. |
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