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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)

Abstract

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.

Keywords

Relation extraction Path attention Knowledge graph 

Notes

Acknowledgement

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).

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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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