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

Hu, Zhiwei, Gutierrez Basulto, Victor ORCID:, 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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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Status: Unpublished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: 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

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