In knowledge graph completion (KGC), predicting triples involving emerging
entities and/or relations, which are unseen when the KG embeddings are learned,
has become a critical challenge. Subgraph reasoning with message passing is a
promising and popular solution. Some recent methods have achieved good
performance, but they (i) usually can only predict triples involving unseen
entities alone, failing to address more realistic fully inductive situations
with both unseen entities and unseen relations, and (ii) often conduct message
passing over the entities with the relation patterns not fully utilized. In
this study, we propose a new method named RMPI which uses a novel Relational
Message Passing network for fully Inductive KGC. It passes messages directly
between relations to make full use of the relation patterns for subgraph
reasoning with new techniques on graph transformation, graph pruning,
relation-aware neighborhood attention, addressing empty subgraphs, etc., and
can utilize the relation semantics defined in the ontological schema of KG.
Extensive evaluation on multiple benchmarks has shown the effectiveness of
techniques involved in RMPI and its better performance compared with the
existing methods that support fully inductive KGC. RMPI is also comparable to
the state-of-the-art partially inductive KGC methods with very promising
results achieved. Our codes and data are available at
this https URL.

本研究提出了一种新方法 RMPI，它使用关系信息直接传递消息来充分利用子图推理中的关系模式，具有一定的归纳能力，并且可以比现有的全归纳 KGC 方法表现更好。

关系消息传递用于完全归纳知识图谱补全

Relational Message Passing for Fully Inductive Knowledge Graph Completion

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 this https URL

本研究提出了一种基于关系消息传递的知识图完成方法，它仅使用边缘特征进行关系预测，该方法在许多知识图数据集上胜过现有的方法，并适用于归纳环境，具有可解释性。