@inproceedings{d2197a1b2559473ba3d477930c5c29cf,
title = "Neighborhood mixture model for knowledge base completion",
abstract = "Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE—a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.",
keywords = "Embedding model, Entity prediction, Knowledge base completion, Link prediction, Mixture model, Relation prediction, Triple classification",
author = "Nguyen, {Dat Quoc} and Kairit Sirts and Lizhen Qu and Mark Johnson",
note = "Publisher Copyright: {\textcopyright} 2016 Association for Computational Linguistics.; 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016 ; Conference date: 11-08-2016 Through 12-08-2016",
year = "2016",
doi = "10.18653/v1/k16-1005",
language = "English",
series = "CoNLL 2016 - 20th SIGNLL Conference on Computational Natural Language Learning, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "40--50",
booktitle = "CoNLL 2016 - 20th SIGNLL Conference on Computational Natural Language Learning, Proceedings",
address = "United States",
}