Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs

• URL: http://arxiv.org/abs/2205.00782v1
• Date: Mon, 2 May 2022 10:05:13 GMT
• Title: Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
• Authors: Zhiwei Hu, V\'ictor Guti\'errez-Basulto, Zhiliang Xiang, Xiaoli Li, Ru Li, Jeff Z. Pan
• Abstract summary: Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem. To address this problem, it has been recently introduced a promising approach based on jointly embedding logical queries and KGs. We propose a novel TypE-aware Message Passing (TEMP) model, which enhances the entity and relation representations in queries.
• Score: 18.56742938427262
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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