Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Knowledge Representation Learning with Entities, Attributes and Relations / 2866
Yankai Lin, Zhiyuan Liu, Maosong Sun

Distributed knowledge representation (KR) encodes both entities and relations in a low-dimensional semantic space, which has significantly promoted the performance of relation extraction and knowledge reasoning. In many knowledge graphs (KG), some relations indicate attributes of entities (attributes) and others indicate relations between entities (relations). Existing KR models regard all relations equally, and usually suffer from poor accuracies when modeling one-to-many and many-to-one relations, mostly composed of attribute. In this paper, we distinguish existing KG-relations into attributes and relations, and propose a new KR model with entities, attributes and relations (KR-EAR). The experiment results show that, by special modeling of attribute, KR-EAR can significantly outperform state-of-the-art KR models in prediction of entities, attributes and relations.