Relation Prediction for Unseen-Entities Using Entity-Word Graphs
Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, Keiichi Nemoto
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.- Anthology ID:
- D19-5302
- Volume:
- Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11–16
- Language:
- URL:
- https://aclanthology.org/D19-5302
- DOI:
- 10.18653/v1/D19-5302
- Cite (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.
- Cite (Informal):
- Relation Prediction for Unseen-Entities Using Entity-Word Graphs (Tagawa et al., TextGraphs 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D19-5302.pdf