基于领域知识图谱的短文本实体链接

doi:10.3778/j.issn.1002-8331.2007-0219

摘要/Abstract

摘要： 实体链接任务是识别文本中潜在的实体指称，并将其链接到给定知识库中无歧义的实体上。在绝大多数情况下，实体链接可能存在中文短文本缺乏有效上下文信息，导致存在一词多义的歧义现象；同时候选链接过程中，候选实体的不确定相关性也影响候选实体链接精确性。针对上述两个问题，提出深度神经网络与关联图相结合的实体链接模型。模型添加字符特征、上下文、信息深层语义来增强指称和实体表示，并进行相似度匹配。利用Fast-newman算法将图谱知识库聚类划分不同类型实体簇，将相似度计算得分最高候选实体所属实体簇映射到关系平面，构建聚类实体关联图。利用偏向随机游走算法考查候选实体之间语义相关度，计算指称与候选实体的匹配程度，输入链接实体。该模型可以实现短文本到知识图谱目标实体的准确链接。

关键词: 实体链接, 神经网络, 关联图, 相似度计算, 语义相关

Abstract: The task of entity linking is to identify potential entity references in the text and link it to unambiguous entity in a given knowledge base. In most cases, the entity link may contain the lack of effective contextual information in the Chinese short text, which leads to the ambiguity of polysemy. In the process of candidate linking, the uncertain correlation of candidate entities also affects the accuracy of candidate linking. Aiming at the above two problems, an entity link model based on the combination of deep neural network and association graph is proposed. The model adds character features, context, and deep semantics of information to enhance the representation of references and entities and performs similarity matching. The Fast-newman algorithm is used to divide the graph knowledge base into different types of entity clusters, and the entity clusters of the candidate entities with the highest similarity calculation scores are mapped to the relationship plane to construct the cluster entity association graph. The biased random walk algorithm examines the semantic relevance between candidate entities, calculates the matching degree between the reference and the candidate entity, and inputs linked entity. The model can realize the accurate link of short text to the target entity of the knowledge graph.

Key words: entity link, neural network, association graph, similarity calculation, semantic relativity

黄金杰, 赵轩伟, 张昕尧, 马敬评, 史宇奇. 基于领域知识图谱的短文本实体链接[J]. 计算机工程与应用, 2022, 58(1): 165-174.

HUANG Jinjie, ZHAO Xuanwei, ZHANG Xinyao, MA Jingping, SHI Yuqi. Short Text Entity Link Based on Domain Knowledge Graph[J]. Computer Engineering and Applications, 2022, 58(1): 165-174.

参考文献

[1] ROTH D，JI H，CHANG M W，et al.Wikification and beyond：The challenges of entity and concept grounding[C]//Proceedings of ACL（Tutorial Abstracts），2014：7.
[2] HUAI B X，BAO T F，ZHU H S，et al.Topic modeling approach to named entity linking[J].Journal of Software，2014，25（9）：2076-2087.
[3] DREDZE M，MCNAMEE P，RAO D，et al.Entity disambiguation for knowledge base population[C]//Proceedings of the 23rd International Conference on Computational Linguistics.Association for Computational Linguistics，2010：277-285.
[4] JI H，GRISHMAN R.Knowledge base population：Successful approaches and challenges[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics：Human Language Technologies，2011：1148-1158.
[5] ZHANG Y，DAI H，KOZAREVA Z，et al.Variational reasoning for question answering with knowledge graph[C]//Proceedings of 32nd AAAI Conference on Artificial Intelligence，2018.
[6] HAN X，SUN L，ZHAO J.Collective entity linking in web text：a graph-based method[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval，2011：765-774.
[7] HE Z，LIU S，LI M，et al.Learning entity representation for entity disambiguation[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics，2013.
[8] SUN Y，LIN L，TANG D，et al.Modeling mention，context and entity with neural networks for entity disambiguation[C]//Proceedings of 24th International Joint Conference on Artificial Intelligence，2015.
[9] FRANCIS-LANDAU M，DURRETT G，KLEIN D.Capturing semantic similarity for entity linking with convolutional neural networks[J].arXiv：1604.00734，2016.
[10] ZENG W，TANG J，ZHAO X.Entity linking on Chinese microblogs via deep neural network[J].IEEE Access，2018，6：25908-25920.
[11] KAWATA N，KIKUI G.Mention detection method for entity linking[C]//Proceedings of 2019 IEEE International Conference on Big Data，2019：41-45.
[12] WANG Q，IWAIHARA M.Deep neural architectures for joint named entity recognition and disambiguation[C]//Proceedings of 2019 IEEE International Conference on Big Data and Smart Computing（BigComp），2019：1-4.
[13] LI Y，SHIJIA E，YANG X.Entity linking for Chinese short texts based on BERT and entity name embeddings[J].Chinese Journal of Information，2019，33（12）：66.
[14] MA C，SHA Y，TAN J，et al.Chinese social media entity linking based on effective context with topic semantics[C]//Proceedings of 2019 IEEE 43rd Annual Computer Software and Applications Conference（COMPSAC），2019：386-395.
[15] SHEN W，WANG J，LUO P，et al.LINDEN：Linking named entities with knowledge base via semantic knowledge[C]//Proceedings of the Web Conference，2012：449-458.
[16] MA B，YANG Y，ZHOU X，et al.Graph-based short text entity linking：A data integration perspective[C]//Proceedings of 2016 International Conference on Asian Language Processing，2016：193-197.
[17] JIA B，WU B，LV J，et al.An entity disambiguation method based on LeaderRank[C]//Proceedings of 2017 IEEE International Conference on Big Data，2017：4337-4342.
[18] ALHELBAWY A，GAIZAUSKAS R.Graph ranking for collective named entity disambiguation[C]//Proceedings of Meeting of the Association for Computational Linguistics，2014.
[19] HAN X，SUN L，ZHAO J.Collective entity linking in Web text：A graph-based method[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval，2011：765-774.
[20] HUANG H，HECK L，JI H.Leveraging deep neural networks and knowledge graphs for entity disambiguation[J].arXiv：1504.07678，2015.
[21] GIRVAN M，NEWMAN M E J.Community structure in social and biological networks[J].Proceedings of the National Academy of Sciences，2002，99（12）：7821-7826.
[22] PENG H，LONG F，DING C.Feature selection based on mutual information criteria of max-dependency，max-relevance，and min-redundancy[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2005，27（8）：1226-1238.
[23] GUPTA S，KENKRE S，TALUKDAR P.CaRe：Open knowledge graph embeddings[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing，2019：378-388.
[24] 周虎，彭韬，张正宇.基于“研教医管”的健康医疗大数据平台创新实践[J].中国卫生事业管理，2019，36（3）161-163.
ZHOU H，PENG T，ZHANG Z Y.Studing on the innovative practice of medical big data platform based on the combining mode of research，teaching and medical management[J].Chinese Health Service Management，2019，36（3）：161-163.
[25] PHAN M C，SUN A，TAY Y，et al.Pair-linking for collective entity disambiguation：Two could be better than all[J].IEEE Transactions on Knowledge and Data Engineering，2019，31（7）：1383-1396.