@inproceedings{tagawa-etal-2019-relation,
title = "Relation Prediction for Unseen-Entities Using Entity-Word Graphs",
author = "Tagawa, Yuki and
Taniguchi, Motoki and
Miura, Yasuhide and
Taniguchi, Tomoki and
Ohkuma, Tomoko and
Yamamoto, Takayuki and
Nemoto, Keiichi",
editor = "Ustalov, Dmitry and
Somasundaran, Swapna and
Jansen, Peter and
Glava{\v{s}}, Goran and
Riedl, Martin and
Surdeanu, Mihai and
Vazirgiannis, Michalis",
booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5302",
doi = "10.18653/v1/D19-5302",
pages = "11--16",
abstract = "Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researches do not focus on the Out-of-KGs entities (Unseen-entities), we need a method that can predict the relation for the entity pairs containing Unseen-entities to automatically add new entities to the KGs. In this study, we focus on relation prediction and propose a method to learn entity representations via a graph structure that uses Seen-entities, Unseen-entities and words as nodes created from the descriptions of all entities. In the experiments, our method shows a significant improvement in the relation prediction for the entity pairs containing Unseen-entities.",
}
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<abstract>Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researches do not focus on the Out-of-KGs entities (Unseen-entities), we need a method that can predict the relation for the entity pairs containing Unseen-entities to automatically add new entities to the KGs. In this study, we focus on relation prediction and propose a method to learn entity representations via a graph structure that uses Seen-entities, Unseen-entities and words as nodes created from the descriptions of all entities. In the experiments, our method shows a significant improvement in the relation prediction for the entity pairs containing Unseen-entities.</abstract>
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%0 Conference Proceedings
%T Relation Prediction for Unseen-Entities Using Entity-Word Graphs
%A Tagawa, Yuki
%A Taniguchi, Motoki
%A Miura, Yasuhide
%A Taniguchi, Tomoki
%A Ohkuma, Tomoko
%A Yamamoto, Takayuki
%A Nemoto, Keiichi
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Jansen, Peter
%Y Glavaš, Goran
%Y Riedl, Martin
%Y Surdeanu, Mihai
%Y Vazirgiannis, Michalis
%S Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F tagawa-etal-2019-relation
%X Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researches do not focus on the Out-of-KGs entities (Unseen-entities), we need a method that can predict the relation for the entity pairs containing Unseen-entities to automatically add new entities to the KGs. In this study, we focus on relation prediction and propose a method to learn entity representations via a graph structure that uses Seen-entities, Unseen-entities and words as nodes created from the descriptions of all entities. In the experiments, our method shows a significant improvement in the relation prediction for the entity pairs containing Unseen-entities.
%R 10.18653/v1/D19-5302
%U https://aclanthology.org/D19-5302
%U https://doi.org/10.18653/v1/D19-5302
%P 11-16
Markdown (Informal)
[Relation Prediction for Unseen-Entities Using Entity-Word Graphs](https://aclanthology.org/D19-5302) (Tagawa et al., TextGraphs 2019)
ACL
- Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, and Keiichi Nemoto. 2019. Relation Prediction for Unseen-Entities Using Entity-Word Graphs. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 11–16, Hong Kong. Association for Computational Linguistics.