Knowledge Representation Learning with Entities, Attributes and Relations

Abstract

Distributed knowledge representation (KR) encodes both entities and relations in a lowdimensional 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 KGrelations 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. The source code of this paper can be obtained from https://github.com/thunlp/KR-EAR.

Extracted Key Phrases

8 Figures and Tables

Cite this paper

@inproceedings{Lin2016KnowledgeRL, title={Knowledge Representation Learning with Entities, Attributes and Relations}, author={Yankai Lin and Zhiyuan Liu and Maosong Sun}, booktitle={IJCAI}, year={2016} }