Improving Entity Disambiguation Using Knowledge Graph Regularization

Zhi Rui Tam, Yi Lun Wu, Hong Han Shuai*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Entity disambiguation plays the role on bridging between words of interest from an input text document and unique entities in a target Knowledge Base (KB). In this study, to address the challenges of global entity disambiguation, we proposed Conditional Masked Entity Model Using Knowledge Graph Regularization (CMEM-KG), based on a conditional masked language model, in which multiple mentions in a context can be disambiguated in one forward pass. In addition, to address the long-tailed distribution of global entity disambiguation, we proposed a link prediction regularization, in which the entity embeddings were jointly learned to predict knowledge graph links to prevent the model from overfitting. Compared to other global entity disambiguation models, the model proposed in this study exhibited improved performance on six public datasets without an iterative decoding.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
EditorsJoão Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031059322
StatePublished - 2022
Event26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022 - Chengdu, China
Duration: 16 May 202219 May 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13280 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022


  • Entity disambiguation
  • Parallel decoding


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