融合实体类别信息的知识图谱表示学习

doi:10.19678/j.issn.1000-3428.0057353

摘要/Abstract

摘要： 知识图谱表示学习通过将实体和关系嵌入连续低维的语义空间中，获取实体和关系的语义关联信息。设计一种融合实体类别信息的类别增强知识图谱表示学习（CEKGRL）模型，构建基于结构与基于类别的实体表示，通过注意力机制捕获实体类别和三元组关系之间的潜在相关性，结合不同实体类别对于某种特定关系的重要程度及实体类别信息进行知识表示学习。在知识图谱补全和三元组分类任务中的实验结果表明，CEKGRL模型在MeanRank和Hit@10评估指标上均取得明显的性能提升，尤其在实体预测任务的Filter设置下相比TKRL模型约分别提升了23.5%和7.2个百分点，具有更好的知识表示学习性能。

关键词: 知识图谱, 知识表示学习, 多源信息融合, 注意力机制, 实体消歧

Abstract: Knowledge graph representation learning embeds both entities and relations into a continuous low-dimensional semantic space,so as to obtain the semantic correlation between entities and relations.This paper proposes a Category-Enhanced Knowledge Graph Representation Learning(CEKGRL) model fused with entity category information.The model constructs structure-based and category-based entity representation,and captures the potential correlation between entity categories and triple relations by introducing the attention mechanism.It combines the different importance of different entity categories for a specific relation and entity category information for Knowledge Representation Learning(KRL).The performance of the model in knowledge graph completion and triple classification tasks is tested,and experimental results show that the CEKGRL model has made significant improvements in the indicators of MeanRank and Hit@10,which are increased by about 23.5% and 7.2 percentage points than the TKRL model in the Filter setting of the entity prediction task.The results indicate that the model has better KRL performance.

Key words: knowledge graph, Knowledge Representation Learning(KRL), multi-source information fusion, attention mechanism, entity disambiguation

中图分类号:

TP391

金婧, 万怀宇, 林友芳. 融合实体类别信息的知识图谱表示学习[J]. 计算机工程, 2021, 47(4): 77-83.

JIN Jing, WAN Huaiyu, LIN Youfang. Knowledge Graph Representation Learning Fused with Entity Category Information[J]. Computer Engineering, 2021, 47(4): 77-83.

http://www.ecice06.com/CN/Y2021/V47/I4/77

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参考文献

[1] 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,USA:ACM Press,2014:601-610.
[2] BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//Proceedings of Annual Conference on Neural Information Processing Systems.Cambridge,USA:MIT Press,2013:2787-2795.
[3] YANG B S,YIH W T,HE X D,et al.Embedding entities and relations for learning and inference in knowledge bases[EB/OL].[2020-01-09].https://arxiv.org/abs/1412.6575.
[4] NEELAKANTAN A,ROTH B,MCCALLUM A.Compositional vector space models for knowledge base completion[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.Philadelphia,USA:ACL Press,2015:156-166.
[5] XIE Ruobing,LIU Zhiyuan,SUN Maosong.Representation learning of knowledge graphs with hierarchical types[C]//Proceedings of International Joint Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2016:2965-2971.
[6] WANG Zhen,ZHANG Jianwen,FENG Jianlin,et al.Know-ledge graph embedding by translating on hyperplanes[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2014:1112-1119.
[7] FANG Yang,ZHAO Xiang,TAN Zhen,et al.A revised translation-based method for knowledge graph representa-tion[J].Journal of Computer Research and Development,2018,55(1):139-150.(in Chinese)方阳,赵翔,谭真,等.一种改进的基于翻译的知识图谱表示方法[J].计算机研究与发展,2018,55(1):139-150.
[8] XIAO Han,HUANG Minlie,HAO Yu,et al.TransA:an adaptive approach for knowledge graph embedding[EB/OL].[2020-02-09].http://arxiv.org/abs/1509.05490.
[9] XIAO Han,HUANG Minlie,HAO Yu,et al.TransG:a generative mixture model for knowledge graph embedd-ing[EB/OL].[2020-02-09].https://arxiv.org/pdf/1509.05488v2.pdf.
[10] LIN Yankai,LIU Zhiyuan,SUN Maosong,et al.Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2015:2181-2187.
[11] JI Guoliang,HE Shizhu,XU Liheng,et al.Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.Philadelphia,USA:ACL Press,2015:687-696.
[12] BORDES A,WESTON J,COLLOBERT R,et al.Learning structured embeddings of knowledge bases[C]//Proceedings of the 25th AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2011:301-306.
[13] BORDES A,GLOROT X,WESTON J,et al.Joint learning of words and meaning representations for open-text semantic parsing[C]//Proceedings of Artificial Intelligence and Statistics Conference.Cambridge,USA:MIT Press,2012:127-135.
[14] BORDES A,GLOROT X,WESTON J,et al.A semantic matching energy function for learning with multi-relational data[J].Machine Learning,2014,94(2):233-259.
[15] NICKEL M,TRESP V,KRIEGEL H P.A three-way model for collective learning on multi-relational data[C]//Proceedings of the 28th International Conference on Machine Learning.Washington D.C.,USA:IEEE Press,2011:809-816.
[16] NICKEL M,TRESP V,KRIEGEL H P.Factorizing YAGO:scalable machine learning for linked data[C]//Proceedings of the 21st International Conference on World Wide Web.New York,USA:ACM Press,2012:271-280.
[17] JENATTON R,ROUX N L,BORDES A,et al.A latent factor model for highly multi-relational data[C]//Proceedings of Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2012:3167-3175.
[18] SOCHER R,CHEN D,MANNING C D,et al.Reasoning with neural tensor networks for knowledge base completion[C]//Proceedings of Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2013:926-934.
[19] XIE Ruobing,LIU Zhiyuan,JIA Jia,et al.Representation learning of knowledge graphs with entity descriptions[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2016:2659-2665.
[20] XIE Ruobing,LIU Zhiyuan,LUAN Huanbo,et al.Image-embodied knowledge representation learning[C]//Pro-ceedings of International Joint Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2017:3140-3146.
[21] WU Jiawei,XIE Ruobing,LIU Zhiyuan,et al.Knowledge representation via joint learning of sequential text and knowledge graphs[EB/OL].[2020-02-09].https://arxiv.org/pdf/1609.07075.pdf.
[22] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Proceedings of Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2017:5998-6008.
[23] KINGMA D,BA J.Adam:a method for stochastic optimization[EB/OL].[2020-02-09].https://arxiv.org/abs/1412.6980.

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