Ontology-Guided and Text-Enhanced Representation for Knowledge Graph Zero-Shot Relational Learning

Abstract

Knowledge graph embedding (KGE) have been proposed and utilized to knowledge graph completion (KGC), but most KGE methods struggle in unseen relations. Previous studies focus on complete zero-shot relational learning by incorporating text-features and proximity relations, which are difficult to accurately represent the complete semantic of relations. To overcome the above-mentioned issues in zero-shot relation learning, we propose an ontology-guided and text-enhanced representation, which could improve the effect of current KGE for unseen relations. In fact, each KG contain ontology and text descriptions that describe the meta-information of knowledge. To combine text-embedding space and graph-embedding space, we design TR-GCN to obtain the meta-representation of relations based on the ontology structure and their textual descriptions. It will be used directly to guide previous KGE methods such as TransE and RotatE on zero-shot relation learning. The experimental results on multiple public datasets demonstrate that the proposed ontology-guided and text-enhanced representation can enrich KGs embedding, and significantly improves the KGC performance on unseen relations.