@inproceedings{61a27e50487e4130b21f182bbf832a73,
title = "Context-aware Entity Typing in Knowledge Graphs",
abstract = "Knowledge graph entity typing aims to infer entities' missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities' contextual information. Specifically, we design two inference mechanisms: i) N2T: independently use each neighbor of an entity to infer its type; ii) Agg2T: aggregate the neighbors of an entity to infer its type. Those mechanisms will produce multiple inference results, and an exponentially weighted pooling method is used to generate the final inference result. Furthermore, we propose a novel loss function to alleviate the false-negative problem during training. Experiments on two real-world KGs demonstrate the effectiveness of our method. The source code and data of this paper can be obtained from https://github.com/ CCIIPLab/CET.",
author = "Weiran Pan and W. Wei and Mao, \{Xian Ling\}",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 ; Conference date: 07-11-2021 Through 11-11-2021",
year = "2021",
doi = "10.18653/v1/2021.findings-emnlp.193",
language = "English",
series = "Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021",
publisher = "Association for Computational Linguistics (ACL)",
pages = "2240--2250",
editor = "Marie-Francine Moens and Xuanjing Huang and Lucia Specia and Yih, \{Scott Wen-Tau\}",
booktitle = "Findings of the Association for Computational Linguistics, Findings of ACL",
address = "United States",
}