Knowledge Representation Learning with Entities, Attributes and Relations

Lin, Yankai; Liu, Zhiyuan; Sun, Maosong

Knowledge Representation Learning with Entities, Attributes and Relations

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2866-2871.

Abstract

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.

Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2016. Knowledge Representation Learning with Entities, Attributes and Relations. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 2866–2871. https://ijcai.org/proceedings/2016/407

@inproceedings{ijcai2016p407,
  title     = {Knowledge Representation Learning with Entities, Attributes and Relations},
  author    = {Lin, Yankai and Liu, Zhiyuan and Sun, Maosong},
  booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-16}},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  editor    = {Subbarao Kambhampati},
  pages     = {2866--2871},
  year      = {2016},
  month     = {7},
  url       = {https://ijcai.org/proceedings/2016/407},
}

TY  - CPAPER
TI  - Knowledge Representation Learning with Entities, Attributes and Relations
AU  - Lin, Yankai
AU  - Liu, Zhiyuan
AU  - Sun, Maosong
T2  - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16
PB  - AAAI Press / International Joint Conferences on Artificial Intelligence
PY  - 2016
SP  - 2866
EP  - 2871
UR  - https://ijcai.org/proceedings/2016/407
ER  -