@inproceedings{zhao-etal-2020-connecting,
title = "Connecting Embeddings for Knowledge Graph Entity Typing",
author = "Zhao, Yu and
Zhang, Anxiang and
Xie, Ruobing and
Liu, Kang and
Wang, Xiaojie",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.572",
doi = "10.18653/v1/2020.acl-main.572",
pages = "6419--6428",
abstract = "Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model via connecting them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. The source code and data of this paper can be obtained from: \url{https://github.com/Adam1679/ConnectE} .",
}
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<abstract>Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model via connecting them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. The source code and data of this paper can be obtained from: https://github.com/Adam1679/ConnectE .</abstract>
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%0 Conference Proceedings
%T Connecting Embeddings for Knowledge Graph Entity Typing
%A Zhao, Yu
%A Zhang, Anxiang
%A Xie, Ruobing
%A Liu, Kang
%A Wang, Xiaojie
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhao-etal-2020-connecting
%X Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model via connecting them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. The source code and data of this paper can be obtained from: https://github.com/Adam1679/ConnectE .
%R 10.18653/v1/2020.acl-main.572
%U https://aclanthology.org/2020.acl-main.572
%U https://doi.org/10.18653/v1/2020.acl-main.572
%P 6419-6428
Markdown (Informal)
[Connecting Embeddings for Knowledge Graph Entity Typing](https://aclanthology.org/2020.acl-main.572) (Zhao et al., ACL 2020)
ACL
- Yu Zhao, Anxiang Zhang, Ruobing Xie, Kang Liu, and Xiaojie Wang. 2020. Connecting Embeddings for Knowledge Graph Entity Typing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6419–6428, Online. Association for Computational Linguistics.