Ontology-guided and Text-enhanced Representation for Knowledge Graph Zero-shot Relational Learning

Ran Song, Shizhu He, Suncong Zheng, Shengxiang Gao, Kang Liu, Jun Zhao, Zhengtao Yu

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.