Relational Message Passing for Knowledge Graph Completion
- URL: http://arxiv.org/abs/2002.06757v2
- Date: Thu, 27 May 2021 21:33:59 GMT
- Title: Relational Message Passing for Knowledge Graph Completion
- Authors: Hongwei Wang, Hongyu Ren, Jure Leskovec
- Abstract summary: We propose a relational message passing method for knowledge graph completion.
It passes relational messages among edges iteratively to aggregate neighborhood information.
Results show our method outperforms stateof-the-art knowledge completion methods by a large margin.
- Score: 78.47976646383222
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowledge graph completion aims to predict missing relations between entities
in a knowledge graph. In this work, we propose a relational message passing
method for knowledge graph completion. Different from existing embedding-based
methods, relational message passing only considers edge features (i.e.,
relation types) without entity IDs in the knowledge graph, and passes
relational messages among edges iteratively to aggregate neighborhood
information. Specifically, two kinds of neighborhood topology are modeled for a
given entity pair under the relational message passing framework: (1)
Relational context, which captures the relation types of edges adjacent to the
given entity pair; (2) Relational paths, which characterize the relative
position between the given two entities in the knowledge graph. The two message
passing modules are combined together for relation prediction. Experimental
results on knowledge graph benchmarks as well as our newly proposed dataset
show that, our method PathCon outperforms state-of-the-art knowledge graph
completion methods by a large margin. PathCon is also shown applicable to
inductive settings where entities are not seen in training stage, and it is
able to provide interpretable explanations for the predicted results. The code
and all datasets are available at https://github.com/hwwang55/PathCon.
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