Multiple Interaction Attention Model for Open-World Knowledge Graph Completion

Chenpeng Fu, Zhixu Li, Qiang Yang, Zhigang Chen, Junhua Fang, Pengpeng Zhao, Jiajie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Knowledge Graph Completion (KGC) aims at complementing missing relationships between entities in a Knowledge Graph (KG). While closed-world KGC approaches utilizing the knowledge within KG could only complement very limited number of missing relations, more and more approaches tend to get knowledge from open-world resources such as online encyclopedias and newswire corpus. For instance, a recent proposed open-world KGC model called ConMask learns embeddings of the entity’s name and parts of its text-description to connect unseen entities to the KG. However, this model does not make full use of the rich feature information in the text descriptions, besides, the proposed relationship-dependent content masking method may easily miss to find the target-words. In this paper, we propose to use a Multiple Interaction Attention (MIA) mechanism to model the interactions between the head entity description, head entity name, the relationship name, and the candidate tail entity descriptions, to form the enriched representations. Our empirical study conducted on two real-world data collections shows that our approach achieves significant improvements comparing to state-of-the-art KGC methods.
Original languageEnglish (US)
Title of host publicationWeb Information Systems Engineering – WISE 2019
PublisherSpringer International Publishing
Pages630-644
Number of pages15
ISBN (Print)9783030342227
DOIs
StatePublished - Nov 14 2019

Cite this