Learning entity type structured embeddings with trustworthiness on noisy knowledge graphs

Abstract

Knowledge graphs (KGs) automatic construction generally involves fine-grained entity typing, i.e., assigning the types to given entities as (entity, entity type). Since the non-negligible inaccuracy of entity typing systems and lack of sufficient human supervision, KGs inevitably face entity type noises. However, most conventional entity type embedding models unreasonably assume that all entity type instances in existing KGs are completely correct, which ignore noises and could lead to potential errors for down-stream tasks. To address this issue, we propose TrustE to build trustworthiness-aware entity type structured embeddings, which takes possible entity type noises into consideration for learning better representations. Specifically, since entities and entity types are completely distinct objects, we encode them in separate entity space and entity type space with a structural projecting matrix, and learn entity type embeddings with tuple trustworthiness. To make the trustworthiness more universal, we only utilize the internal structural knowledge in existing KGs and build two tuple trustworthiness considering the local tuple and global triple information respectively, which correspondingly makes it more challenging due to the limited knowledge. We evaluate our models on three tasks: entity type noise detection, entity type prediction and classification. Experimental results on real-world datasets (FB15kET and YAGO43kET) show that our models outperform all baselines on all tasks, which verify the capability of TrustE in learning better entity type structural embeddings on noisy KGs. The source code and data of this paper can be obtained from https://github.com/Quan-SWUFE/TrustE