TY - GEN
T1 - Improving Entity Disambiguation Using Knowledge Graph Regularization
AU - Tam, Zhi Rui
AU - Wu, Yi Lun
AU - Shuai, Hong Han
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Entity disambiguation
KW - Parallel decoding
UR - http://www.scopus.com/inward/record.url?scp=85130347520&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-05933-9_27
DO - 10.1007/978-3-031-05933-9_27
M3 - Conference contribution
AN - SCOPUS:85130347520
SN - 9783031059322
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 341
EP - 353
BT - Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
A2 - Gama, João
A2 - Li, Tianrui
A2 - Yu, Yang
A2 - Chen, Enhong
A2 - Zheng, Yu
A2 - Teng, Fei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022
Y2 - 16 May 2022 through 19 May 2022
ER -