Entity Alignment Method for Different Knowledge Repositories with Joint Semantic Representation
Li Wenna1,2,Zhang Zhixiong1,2,3()
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China 2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China 3Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071, China
[Objective] This paper combines the structure and semantic information of knowledge, aiming to create a better entity alignment method for different knowledge repositories. [Methods] First, we used the TransE model to represent the structure of entities, and used the BERT model to represent their semantic information. Then, we designed an entity alignment method based on the BTJE model (BERT and TransE Joint model for Entity alignment). Finally, we use the siamese network model to finish entity alignment tasks. [Results] We examined the new method with DBP-WD and DBP-YG datasets. Their optimal MRR values reached 0.521 and 0.413, while the Hits@1 reached 0.542 and 0.478. These results were better than those of the traditional models. [Limitations] The size of our experimental data set needs to be expanded, which will further evaluate the performance of the proposed method. [Conclusions] Our new method could effectively finish entity alignment tasks for different knowledge bases.
李文娜, 张智雄. 基于联合语义表示的不同知识库中的实体对齐方法研究*[J]. 数据分析与知识发现, 2021, 5(7): 1-9.
Li Wenna, Zhang Zhixiong. Entity Alignment Method for Different Knowledge Repositories with Joint Semantic Representation. Data Analysis and Knowledge Discovery, 2021, 5(7): 1-9.
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]
Auer S, Bizer C, Kobilarov G, et al. DBpedia: A Nucleus for a Web of Open Data[C]// Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference. Springer, Berlin, Heidelberg, 2007: 722-735.
[4]
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. 2014: 601-610.
[5]
Scharffe F, Liu Y B, Zhou C G. RDF-AI: An Architecture for RDF Datasets Matching, Fusion and Interlink[C]// Proceedings of IJCAI 2009 Workshop on Identity, Reference, and Knowledge Representation(IR-KR). 2009.
[6]
Volz J, Bizer C, Gaedke M, et al. Discovering and Maintaining Links on the Web of Data[C]// Proceedings of the 8th International Semantic Web Conference. 2009:650-665.
[7]
Ngomo A C N, Auer S. LIMES : A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data[C]// Proceedings of the 22nd International Joint Conference on Artificial Intelligence. AAAI Press, 2011: 2312-2317.
[8]
Han J W, Kamber M, Pei J. Data Mining: Concepts and Techniques[M]. The 3rd Edition. The Morgan Kaufmann, 2011.
[9]
Vapnik V. The Nature of Statistical Learning Theory[M]. Springer Science & Business Media, 2013.
[10]
Bhattacharya I, Getoor L. A Latent Dirichlet Model for Unsupervised Entity Resolution[C]// Proceedings of the 6th SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2006: 47-58.
[11]
Hall R, Sutton C, McCallum A. Unsupervised Deduplication Using Cross-field Dependencies[C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008: 310-317.
[12]
Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3:993-1022.
[13]
Wang Z C, Lv Q S, Lan X H, et al. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 349-357.
[14]
Wu Y T, Liu X, Feng Y S, et al. Relation-aware Entity Alignment for Heterogeneous Knowledge Graphs[OL]. arXiv Preprint, arXiv: 1908. 08210.
[15]
Zhang Q H, Sun Z Q, Hu W, et al. Multi-view Knowledge Graph Embedding for Entity Alignment[OL]. arXiv Preprint, arXiv: 1906. 02390.
[16]
Le Q, Mikolov T. Distributed Representations of Sentences and Documents[C]// Proceedings of the 31st International Conference on Machine Learning. 2014: 1188-1196.
[17]
Bordes A, Usunier N, Garcia-Duran A, et al. Translating Embeddings for Modeling Multi-relational Data[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013: 2787-2795.
[18]
Lin Y K, Liu Z Y, Sun M S, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015: 2181-2187.
[19]
Wang Z, Zhang J W, Feng J L, et al. Knowledge Graph Embedding by Translating on Hyperplanes[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014: 1112-1119.
[20]
Chen M H, Tian Y T, Yang M H, et al. Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment[OL]. arXiv Preprint, arXiv:1611.03954.
[21]
Zhu H, Xie R B, Liu Z Y, et al. Iterative Entity Alignment via Joint Knowledge Embeddings[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017: 4258-4264.
[22]
Lin Y K, Liu Z Y, Sun M S. Modeling Relation Paths for Representation Learning of Knowledge Bases[OL]. arXiv Preprint, arXiv:1506.00379.
[23]
Sun Z Q, Hu W, Zhang Q H, et al. Bootstrapping Entity Alignment with Knowledge Graph Embedding[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018: 4396-4402.
[24]
Sun Z Q, Hu W, Li C K. Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding[C]// Proceedings of International Semantic Web Conference. Springer, Cham, 2017: 628-644.
[25]
Chen M H, Tian Y T, Chang K W, et al. Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment[OL]. arXiv Preprint, arXiv:1806.06478.
[26]
Chung J, Gulcehre C, Cho K H, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[OL]. arXiv Preprint, arXiv:1412.3555.
[27]
Trsedya B D, Qi J Z, Zhang R. Entity Alignment Between Knowledge Graphs Using Attribute Embeddings[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2019: 297-304.
[28]
Hochreiter S, Schmidhuber J. Long Short-Term Memory[J]. Neural Computation, 1997, 9(8):1735-1780.
pmid: 9377276
[29]
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of NAACL-HLT 2019. 2019:4171-4186.
[30]
Bromley J, Bentz J W, Bottou L, et al. Signature Verification Using a “Siamese” Time Delay Neural Network[J]. International Journal of Pattern Recognition and Artificial Intelligence, 1993, 7(4):669-688.
doi: 10.1142/S0218001493000339
[31]
Vrandečić D. Wikidata: A New Platform for Collaborative Data Collection[C]// Proceedings of the 21st International Conference on World Wide Web. 2012: 1063-1064.
