Relational Message Passing for Knowledge Graph Completion

TL;DR

This paper introduces PathCon, a relational message passing approach for knowledge graph completion that leverages edge features and neighborhood topology, outperforming existing methods and providing interpretability.

Contribution

It proposes a novel relational message passing framework that models neighborhood topology without relying on entity IDs, improving accuracy and interpretability in knowledge graph completion.

Findings

PathCon outperforms state-of-the-art methods on benchmark datasets.

It is effective in inductive settings with unseen entities.

Provides interpretable explanations for predictions.

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.