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Multiple Interaction Attention Model for Open-World Knowledge Graph Completion

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Web Information Systems Engineering – WISE 2019 (WISE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11881))

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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.

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This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016, 61402313, 61472263), and Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003), and this is a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Correspondence to Zhixu Li .

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Fu, C. et al. (2019). Multiple Interaction Attention Model for Open-World Knowledge Graph Completion. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham.

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  • Print ISBN: 978-3-030-34222-7

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