Abstract
Inductive Knowledge Graph Completion (KGC) poses challenges due to the absence of emerging entities during training. Current methods utilize Graph Neural Networks (GNNs) to learn and propagate entity representations, achieving notable performance. However, these approaches primarily focus on chain-based logical rules, limiting their ability to capture the rich semantics of knowledge graphs. To address this challenge, we propose to generate Graph-based Rules for Enhancing Logical Reasoning (GRELR), a novel framework that leverages graph-based rules for enhanced reasoning. GRELR formulates graph-based rules by extracting relevant subgraphs and fuses them to construct comprehensive relation representations. This approach, combined with subgraph reasoning, significantly improves inference capabilities and showcases the potential of graph-based rules in inductive KGC. To demonstrate the effectiveness of the GRELR framework, we conduct experiments on three benchmark datasets, and our approach achieves state-of-the-art performance.
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Acknowledgments
This research is supported by National Key R&D Program of China under Grant No.2021ZD0111902; National Natural Science Foundation of China under Grant No.62206007, U21B2038, U19B2039, 62172023; R&D Program of Beijing Municipal Education Commission KZ202210005008, Beijing Natural Science Foundation 4222021, and Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education.
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Sun, K., Jiang, H., Hu, Y., Yin, B. (2024). Generating Graph-Based Rules for Enhancing Logical Reasoning. In: Huang, DS., Pan, Y., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14873. Springer, Singapore. https://doi.org/10.1007/978-981-97-5615-5_12
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