Attention-Aware Path-Based Relation Extraction for Medical Knowledge Graph

  • Desi Wen
  • Yong Liu
  • Kaiqi Yuan
  • Shangchun Si
  • Ying ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10699)


The task of entity relation extraction discovers new relation facts and enables broader applications of knowledge graph. Distant supervision is widely adopted for relation extraction, which requires large amounts of texts containing entity pairs as training data. However, in some specific domains such as medical-related applications, entity pairs that have certain relations might not appear together, thus it is difficult to meet the requirement for distantly supervised relation extraction. In the light of this challenge, we propose a novel path-based model to discover new entity relation facts. Instead of finding texts for relation extraction, the proposed method extracts path-only information for entity pairs from the current knowledge graph. For each pair of entities, multiple paths can be extracted, and some of them are more useful for relation extraction than others. In order to capture this observation, we employ attention mechanism to assign different weights for different paths, which highlights the useful paths for entity relation extraction. To demonstrate the effectiveness of the proposed method, we conduct various experiments on a large-scale medical knowledge graph. Compared with the state-of-the-art relation extraction methods using the structure of knowledge graph, the proposed method significantly improves the accuracy of extracted relation facts and achieves the best performance.


Relation extraction Path attention Knowledge graph 



This work was financially supported by the National Natural Science Foundation of China (No. 61602013), and the Shenzhen Key Fundamental Research Projects (Grant No. JCYJ20160330095313861, and JCYJ20151030154330711).


  1. 1.
    Yin, J., Jiang, X., Lu, Z., Shang, L., Li, H., Li, X.: Neural generative question answering, vol. 27, pp. 2972–2978 (2015)Google Scholar
  2. 2.
    Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353–362 (2016)Google Scholar
  3. 3.
    Mintz, M., Bills, S., Snow, R., Dan, J.: Distant supervision for relation extraction without labeled data. In: Joint Conference of the, Meeting of the ACL and the, International Joint Conference on Natural Language Processing of the AFNLP: Volume 2, pp. 1003–1011 (2009)Google Scholar
  4. 4.
    Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Meeting of the Association for Computational Linguistics, pp. 2124–2133 (2016)Google Scholar
  5. 5.
    Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures (2016)Google Scholar
  6. 6.
    Zeng, W., Lin, Y., Liu, Z., Sun, M.: Incorporating relation paths in neural relation extraction (2016)Google Scholar
  7. 7.
    Bordes, A., Usunier, N., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: International Conference on Neural Information Processing Systems, pp. 2787–2795 (2013)Google Scholar
  8. 8.
    Feng, J.: Knowledge graph embedding by translating on hyperplanes. In: AAAI (2014)Google Scholar
  9. 9.
    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 and the 7th International Joint Conference on Natural Language Processing, pp. 687–696 (2015)Google Scholar
  10. 10.
    Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 985–991 (2016)Google Scholar
  11. 11.
    Shao, Y., Kai, L., Lei, C., Zi, H., Cui, B., Liu, Z., et al.: Fast parallel path concatenation for graph extraction. IEEE Trans. Knowl. Data Eng. 99 (2017)Google Scholar
  12. 12.
    Das, R., Neelakantan, A., Belanger, D., Mccallum, A.: Incorporating selectional preferences in multi-hop relation extraction. In: The Workshop on Automated Knowledge Base Construction, pp. 18–23 (2016)Google Scholar
  13. 13.
    Shen, Y., Colloc, J., Jacquet-Andrieu, A., Lei, K.: Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system. J. Biomed. Inform. 56(C), 307–317 (2015)CrossRefGoogle Scholar
  14. 14.
    Cho, K., Merrienboer, B.V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Computer Science (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Desi Wen
    • 1
  • Yong Liu
    • 2
  • Kaiqi Yuan
    • 1
  • Shangchun Si
    • 1
  • Ying Shen
    • 1
    Email author
  1. 1.Shenzhen Key Lab for Cloud Computing Technology and Applications, School of Electronic and Computer Engineering (SECE), Institute of Big Data TechnologiesPeking UniversityShenzhenPeople’s Republic of China
  2. 2.IER Business Development CenterShenzhenPeople’s Republic of China

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