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Understanding Expressivity of GNN in Rule Learning
April 11, 2024, 4:42 a.m. | Haiquan Qiu, Yongqi Zhang, Yong Li, Quanming Yao
cs.LG updates on arXiv.org arxiv.org
Abstract: Rule learning is critical to improving knowledge graph (KG) reasoning due to their ability to provide logical and interpretable explanations. Recently, Graph Neural Networks (GNNs) with tail entity scoring achieve the state-of-the-art performance on KG reasoning. However, the theoretical understandings for these GNNs are either lacking or focusing on single-relational graphs, leaving what the kind of rules these GNNs can learn an open problem. We propose to fill the above gap in this paper. Specifically, …
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