A Neighborhood Re-Ranking Model With Relation Constraint for Knowledge Graph Completion

Abstract

Knowledge graph completion (KGC) aims to predict missing links based on observed triples. However, current KGC models are still limited by the following two aspects. (1) the entity semantics is implicitly learned by neural network and merely depends on existing facts, which mostly suffers from less additional specific knowledge. Although previous studies have noticed that entity type information can effectively improve KGC task, most of them rely on labeled type-specific data. (2) the recent graph-based models mainly concentrate on Graph Neural Network (GNN) to update source entity representation, regardless of the separate role that neighborhood information plays and may mix noisy neighbor features for target prediction. To address the above two issues, we propose a neighborhood re-ranking model with relation constraint for KGC task. We suggest that both relation constraint and structured information located in triples can boost the model performance. More importantly, we automatically generate explicit constraints as additional type feature to enrich entity representation instead of depending on human annotated labels. Meanwhile, we construct a neighborhood completion module to re-rank candidate entities for full use of the neighbor structure rather than traditional GNN updating manner. Extensive experiments on seven benchmarks demonstrate that our model achieves the competitive results in comparison to the recent advanced baselines.