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.
Original language | English |
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Title of host publication | Proceedings of the 31st International Joint Conference on Artifical Intelligence, IJCAI-ECAI 2022 |
Editors | Luc De Raedt |
Publisher | International Joint Conferences on Artificial Intelligence Organization |
Pages | 3078-3084 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-956792-00-3 |
DOIs | |
Publication status | Published - 23 Jul 2022 |
Event | 31st International Joint conference on Artificial Intelligence - Vienna, Austria Duration: 23 Jul 2022 → 29 Jul 2022 https://ijcai-22.org/ |
Conference
Conference | 31st International Joint conference on Artificial Intelligence |
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Abbreviated title | IJCAI-ECAI 2022 |
Country/Territory | Austria |
City | Vienna |
Period | 23/07/22 → 29/07/22 |
Internet address |