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

Page view(s)
5
Checked on Aug 22, 2022
Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Title:
Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Other Titles:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Keywords:
Publication Date:
16 July 2022
Citation:
Hu, Z., Gutierrez Basulto, V., Xiang, Z., Li, X., Li, R., & Z. Pan, J. (2022). Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/427
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. Recently, to address this problem a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities has emerged. 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 model, TypE-aware Message Passing (TEMP), which enhances the entity and relation representation in queries, and simultaneously improves generalization, and 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.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: I2R CORE FUNDING
Grant Reference no. : Nil

This work has been supported by the National Key Research and Development Program of China (No.2020AAA0106100), by the National Natural Science Foundation of China (No.61936012), by the Chang Jiang Scholars Program (J2019032), by a Leverhulme Trust Research Project Grant (RPG-2021-140), and by the Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS) at Cardiff University.
Description:
ISSN:
Nil
Files uploaded:

Files uploaded: