Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract)

Authors

  • Ziyue Qiao Computer Network Information Center, Chinese Academy of Sciences University of Chinese Academy of Sciences
  • Zhiyuan Ning Computer Network Information Center, Chinese Academy of Sciences University of Chinese Academy of Sciences
  • Yi Du Computer Network Information Center, Chinese Academy of Sciences
  • Yuanchun Zhou Computer Network Information Center, Chinese Academy of Sciences

Keywords:

Knowledge Graph Completion, Knowledge Graph Embedding, Relation Predicion

Abstract

Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the contextual information of entity and relation. Here, we extract contexts of entities and relations from the triplets which they compose. We propose a model named AggrE, which conducts efficient aggregations respectively on entity context and relation context in multi-hops, and learns context-enhanced entity and relation embeddings for knowledge graph completion. The experiment results show that AggrE is competitive to existing models.

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Published

2021-05-18

How to Cite

Qiao, Z., Ning, Z., Du, Y., & Zhou, Y. (2021). Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15871-15872. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17932

Issue

Section

AAAI Student Abstract and Poster Program