Skip to main content
SpringerLink
Log in

Entity-related paths modeling for knowledge base completion

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

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 EP-TransE, 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J. Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2008, 1247–1250

  2. Miller G. Wordnet: a lexical database for english. Future Generation Computer Systems, 1995, 38(11): 39–41

    Google Scholar 

  3. Mendes P, Jakob M, Bizer C. DBpedia: a multilingual cross-domain knowledge base. In: Proceedings of the 8th International Conference on Language Resources and Evaluation. 2012, 1813–1817

  4. Wen D, Liu Y, Yuan K, Si S C, Shen Y. Attention-aware path-based relation extraction for medical knowledge graph. In: Proceedings of International Conference on Smart Computing and Communication. 2017, 321–331

  5. Gesmundo A, Hall K. 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

  6. Zheng W, Yu J X, Zou L. Question answering over knowledge graphs: question understanding via template decomposition. Proceedings of the VLDB Endowment, 2018, 11(11): 1373–1386

    Article  Google Scholar 

  7. Chen W, Zhang X, Wang T, Yang B, Li Y. Option-aware knowledge graph for political ideology detection. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 3647–3653

  8. Bordes A, Usunier N, Garcia A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013, 2787–2795

  9. Lin Y, Liu Z, Luan H, Sun M, Rao S, Liu S. Modeling relation paths for representation learning of knowledge bases. In: Proceedings of Conference on Empirical Methods in Natural Language Processing. 2015, 705–714

  10. Bordes A, Weston J, Collobert R, Bengio Y. Learning structured embeddings of knowledge bases. In: Proceedings of AAAI Conference on Artificial Intelligence. 2011, 301–306

  11. Bordes A, Glorot X, Weston J, Bengio Y. A semantic matching energy function for learning with multi-relational data. Machine Learning, 2011, 94(2): 233–259

    Article  MathSciNet  Google Scholar 

  12. Wang Z, Zhang J, Feng J, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI Conference on Artificial Intelligence. 2014, 1112–1119

  13. Lin Y, Liu Z, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI Conference on Artificial Intelligence. 2015, 2187–2195

  14. Ji G, He S, Xu L, Liu K, Zhao J. Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. 2015, 687–696

  15. He S, Liu K, Ji G, Zhao J. Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015, 623–632

  16. Ji G, Liu K, He S, Zhao J. Knowledge graph completion with adaptive sparse transfer matrix. In: Proceedings of AAAI Conference on Artificial Intelligence. 2016, 985–991

  17. Xiao H, Huang M, Zhu X. Transg: a generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016, 2316–2325

  18. Lin Y, Liu Z, Sun M. Knowledge representation learning with entities, attributes and relations. In: Proceedings of International Joint Conference on Artificial Intelligence. 2016, 2866–2872

  19. Zhu J, Jia Y, Qiao J. Modeling the correlations of relations for knowledge graph embedding. Journal of Computer Science and Technology, 2018, 33(2): 323–334

    Article  MathSciNet  Google Scholar 

  20. Zhang M, Wang Q, Xu W, Li W, Sun S. Discriminative path-based knowledge graph embedding for precise link prediction. In: Proceedings of European Conference on Information Retrieval. 2018, 276–288

  21. Lin X, Liang Y, Giunchiglia F, Feng X, Guan R. Relation path embedding in knowledge graphs. Neural Computing and Applications, 2019, 31: 5629–5639

    Article  Google Scholar 

  22. Wang Z, Rong E, Zhuo H, Zhu H. Embedding knowledge graphs based on transitivity and asymmetry of rules. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2018, 141–153

Download references

Acknowledgements

This work was supported by the National Key Research and Development Plan of China (2017YFD0400101), the National Natural Science Foundation of China (Grant No. 61502294) and the Natural Science Foundation of Shanghai, Project Number (16ZR1411200).

Author information

Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Fangfang Liu, Yan Shen & Tienan Zhang

  2. Computing Center, Shanghai University, Shanghai, 200444, China

    Honghao Gao

Authors
  1. Fangfang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tienan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Honghao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Honghao Gao.

Additional information

Fangfang Liu received her PhD degree from Fudan University, China in 2007. She is currently an assistant professor in the School of Computer Engineering and Science, Shanghai University, China. Her main research interests include knowledge representation, knowledge reasoning, and Web service.

Yan Shen is currently working toward the master degree in School of Computer Engineering and Science, Shanghai University, China. Her research interests include knowledge base, knowledge graph, and natural language processing.

Tienan Zhang is currently working toward the master degree in School of Computer Engineering and Science, Shanghai University, China. His research interests include knowledge base population, service computing, and sentiment analysis.

Honghao Gao received the PhD degree in Computer Science and started his academic career at Shanghai University, China in 2012. He is an IET Fellow, BCS Fellow, EAI Fellow, IEEE Senior Member, CCF Senior Member, and CAAI Senior Member. His research interests include service computing, model checking-based software verification, and sensors data application.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, F., Shen, Y., Zhang, T. et al. Entity-related paths modeling for knowledge base completion. Front. Comput. Sci. 14, 145311 (2020). https://doi.org/10.1007/s11704-019-8264-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-019-8264-4

Keywords

Search

Navigation