Type-aware embeddings for multi-hop reasoning over knowledge graphs

Hu, Zhiwei, Gutierrez Basulto, Victor ORCID: https://orcid.org/0000-0002-6117-5459, Xiang, Zhiliang, Li, Xiaoli, Li, Ru and Pan, Jeff Z. 2022. Type-aware embeddings for multi-hop reasoning over knowledge graphs. Presented at: 31st International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2022), Vienna, Austria, 23-29 July 2022.

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Abstract

Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. To address this problem, it has been recently introduced a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities. However, existing proposals ignore critical semantic knowledge inherently available in KGs, such as type information. To leverage type information, we propose a novel TypE-aware Message Passing (TEMP) model, which enhances the entity and relation representations in queries, and simultaneously improves generalization, deductive and inductive reasoning. Remarkably, TEMP is a plug-and-play model that can be easily incorporated into existing embedding-based models to improve their performance. Extensive experiments on three real-world datasets demonstrate TEMP’s effectiveness.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Computer Science & Informatics
Funders:	Leverhulme Trust Research Project Grant (RPG-2021-140)
Date of First Compliant Deposit:	6 May 2022
Last Modified:	30 Nov 2022 07:42
URI:	https://orca.cardiff.ac.uk/id/eprint/149582

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