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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2020, Vol. 14 Issue (5) : 145311    https://doi.org/10.1007/s11704-019-8264-4
RESEARCH ARTICLE
Entity-related paths modeling for knowledge base completion
Fangfang LIU1, Yan SHEN1, Tienan ZHANG1, Honghao GAO2()
1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2. Computing Center, Shanghai University, Shanghai 200444, China
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Abstract

Knowledge bases (KBs) are far from complete, necessitating a demand for KB completion. Among various methods, embedding has received increasing attention in recent years. PTransE, an important approach using embedding method in KB completion, considers multiple-step relation paths based on TransE, but ignores the association between entity and their related entities with the same direct relationships. In this paper, we propose an approach called EPTransE, which considers this kind of association. As a matter of fact, the dissimilarity of these related entities should be taken into consideration and it should not exceed a certain threshold. EPTransE adjusts the embedding vector of an entity by comparing it with its related entities which are connected by the same direct relationship. EPTransE further makes the euclidean distance between them less than a certain threshold. Therefore, the embedding vectors of entities are able to contain rich semantic information, which is valuable for KB completion. In experiments, we evaluated our approach on two tasks, including entity prediction and relation prediction. Experimental results show that our idea of considering the dissimilarity of related entities with the same direct relationships is effective.

Keywords KB completion      related entity      embedding representation      relation path      translation operation     
Corresponding Author(s): Honghao GAO   
Just Accepted Date: 30 August 2019   Issue Date: 10 March 2020
 Cite this article:   
Fangfang LIU,Yan SHEN,Tienan ZHANG, et al. Entity-related paths modeling for knowledge base completion[J]. Front. Comput. Sci., 2020, 14(5): 145311.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-8264-4
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I5/145311
1 K Bollacker, C Evans, P Paritosh, T Sturge, J Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2008, 1247–1250
https://doi.org/10.1145/1376616.1376746
2 G Miller. Wordnet: a lexical database for english. Future Generation Computer Systems, 1995, 38(11): 39–41
https://doi.org/10.1145/219717.219748
3 P Mendes, M Jakob, C Bizer. DBpedia: a multilingual cross-domain knowledge base. In: Proceedings of the 8th International Conference on Language Resources and Evaluation. 2012, 1813–1817
4 D Wen, Y Liu, K Yuan, S C Si, Y Shen. Attention-aware path-based relation extraction for medical knowledge graph. In: Proceedings of International Conference on Smart Computing and Communication. 2017, 321–331
https://doi.org/10.1007/978-3-319-73830-7_32
5 A Gesmundo, K Hall. Projecting the knowledge graph to syntactic parsing. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. 2014, 28–32
https://doi.org/10.3115/v1/E14-4006
6 W Zheng, J X Yu, L Zou. Question answering over knowledge graphs: question understanding via template decomposition. Proceedings of the VLDB Endowment, 2018, 11(11): 1373–1386
https://doi.org/10.14778/3236187.3236192
7 W Chen, X Zhang, T Wang, B Yang, Y Li. Option-aware knowledge graph for political ideology detection. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 3647–3653
https://doi.org/10.24963/ijcai.2017/510
8 A Bordes, N Usunier, A Garcia, J Weston, O Yakhnenko. Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013, 2787–2795
9 Y Lin, Z Liu, H Luan, M Sun, S Rao, S Liu. Modeling relation paths for representation learning of knowledge bases. In: Proceedings of Conference on Empirical Methods in Natural Language Processing. 2015, 705–714
https://doi.org/10.18653/v1/D15-1082
10 A Bordes, J Weston, R Collobert, Y Bengio. Learning structured embeddings of knowledge bases. In: Proceedings of AAAI Conference on Artificial Intelligence. 2011, 301–306
11 A Bordes, X Glorot, J Weston, Y Bengio. A semantic matching energy function for learning with multi-relational data. Machine Learning, 2011, 94(2): 233–259
https://doi.org/10.1007/s10994-013-5363-6
12 Z Wang, J Zhang, J Feng, Z Chen. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI Conference on Artificial Intelligence. 2014, 1112–1119
13 Y Lin, Z Liu, X Zhu. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI Conference on Artificial Intelligence. 2015, 2187–2195
14 G Ji, S He, L Xu, K Liu, J Zhao. Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. 2015, 687–696
https://doi.org/10.3115/v1/P15-1067
15 S He, K Liu, G Ji, J Zhao. Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015, 623–632
https://doi.org/10.1145/2806416.2806502
16 G Ji, K Liu, S He, J Zhao. Knowledge graph completion with adaptive sparse transfer matrix. In: Proceedings of AAAI Conference on Artificial Intelligence. 2016, 985–991
17 H Xiao, M Huang, X Zhu. Transg: a generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016, 2316–2325
https://doi.org/10.18653/v1/P16-1219
18 Y Lin, Z Liu, M Sun. Knowledge representation learning with entities, attributes and relations. In: Proceedings of International Joint Conference on Artificial Intelligence. 2016, 2866–2872
19 J Zhu, Y Jia, J Qiao. Modeling the correlations of relations for knowledge graph embedding. Journal of Computer Science and Technology, 2018, 33(2): 323–334
https://doi.org/10.1007/s11390-018-1821-8
20 M Zhang, Q Wang, W Xu, W Li, S Sun. Discriminative path-based knowledge graph embedding for precise link prediction. In: Proceedings of European Conference on Information Retrieval. 2018, 276–288
https://doi.org/10.1007/978-3-319-76941-7_21
21 X Lin, Y Liang, F Giunchiglia, X Feng, R Guan. Relation path embedding in knowledge graphs. Neural Computing and Applications, 2019, 31: 5629–5639
https://doi.org/10.1007/s00521-018-3384-6
22 Z Wang, E Rong, H Zhuo, H Zhu. Embedding knowledge graphs based on transitivity and asymmetry of rules. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and DataMining. 2018, 141–153
https://doi.org/10.1007/978-3-319-93037-4_12
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