Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 3, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa110
Keywords
biomedical information extraction; language model; neural network; knowledge graph
Funding
- National Natural Science Foundation of China [61702121, 61772378]
- Research Foundation of Ministry of Education of China [18JZD015]
- Key Project of State Language Commission of China [ZDI135-112]
- Guangdong Basic and Applied Basic Research Foundation [2020A151501705]
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Biomedical information extraction (BioIE) aims to analyze biomedical texts and extract structured information, incorporating external structural knowledge can significantly improve performance, especially when integrating a large scale of biomedical knowledge graphs (BioKGLM).
Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. However, they neglect to incorporate external structural knowledge, which can provide rich factual information to support the underlying understanding and reasoning for biomedical information extraction. In this paper, we first evaluate current extraction methods, including vanilla neural networks, general language models and pre-trained contextualized language models on biomedical information extraction tasks, including named entity recognition, relation extraction and event extraction. We then propose to enrich a contextualized language model by integrating a large scale of biomedical knowledge graphs (namely, BioKGLM). In order to effectively encode knowledge, we explore a three-stage training procedure and introduce different fusion strategies to facilitate knowledge injection. Experimental results on multiple tasks show that BioKGLM consistently outperforms state-of-the-art extraction models. A further analysis proves that BioKGLM can capture the underlying relations between biomedical knowledge concepts, which are crucial for BioIE.
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