Knowledge Graph Embedding with Numeric Attributes of Entities

Yanrong Wu, Zhichun Wang


Abstract
Knowledge Graph (KG) embedding projects entities and relations into low dimensional vector space, which has been successfully applied in KG completion task. The previous embedding approaches only model entities and their relations, ignoring a large number of entities’ numeric attributes in KGs. In this paper, we propose a new KG embedding model which jointly model entity relations and numeric attributes. Our approach combines an attribute embedding model with a translation-based structure embedding model, which learns the embeddings of entities, relations, and attributes simultaneously. Experiments of link prediction on YAGO and Freebase show that the performance is effectively improved by adding entities’ numeric attributes in the embedding model.
Anthology ID:
W18-3017
Volume:
Proceedings of The Third Workshop on Representation Learning for NLP
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | RepL4NLP | WS
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
132–136
Language:
URL:
https://www.aclweb.org/anthology/W18-3017
DOI:
10.18653/v1/W18-3017
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3017.pdf