Knowledge Graph Reasoning with Relational Directed Graph
- URL: http://arxiv.org/abs/2108.06040v1
- Date: Fri, 13 Aug 2021 03:27:01 GMT
- Title: Knowledge Graph Reasoning with Relational Directed Graph
- Authors: Yongqi Zhang and Quanming Yao
- Abstract summary: Methods based on the relational path in the literature have shown strong, interpretable, and inductive reasoning ability.
We introduce a novel relational structure, i.e., relational directed graph (r-digraph), which is composed of overlapped relational paths.
We propose a variant of graph neural network, i.e., RED-GNN, to address the challenges by learning the RElational Digraph with a variant of GNN.
- Score: 40.555874504438506
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reasoning on the knowledge graph (KG) aims to infer new facts from existing
ones. Methods based on the relational path in the literature have shown strong,
interpretable, and inductive reasoning ability. However, the paths are
naturally limited in capturing complex topology in KG. In this paper, we
introduce a novel relational structure, i.e., relational directed graph
(r-digraph), which is composed of overlapped relational paths, to capture the
KG's structural information. Since the digraph exhibits more complex structure
than paths, constructing and learning on the r-digraph are challenging. Here,
we propose a variant of graph neural network, i.e., RED-GNN, to address the
above challenges by learning the RElational Digraph with a variant of GNN.
Specifically, RED-GNN recursively encodes multiple r-digraphs with shared edges
and selects the strongly correlated edges through query-dependent attention
weights. We demonstrate the significant gains on reasoning both KG with unseen
entities and incompletion KG benchmarks by the r-digraph, the efficiency of
RED-GNN, and the interpretable dependencies learned on the r-digraph.
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