BioRel: towards large-scale biomedical relation extraction
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Open Access
RESEARCH
BioRel: towards large‑scale biomedical relation extraction Rui Xing, Jie Luo* and Tengwei Song
From Biological Ontologies and Knowledge bases workshop 2019 San Diego, CA, USA. 18-21 November 2019 *Correspondence: [email protected] State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
Abstract Background: Although biomedical publications and literature are growing rapidly, there still lacks structured knowledge that can be easily processed by computer programs. In order to extract such knowledge from plain text and transform them into structural form, the relation extraction problem becomes an important issue. Datasets play a critical role in the development of relation extraction methods. However, existing relation extraction datasets in biomedical domain are mainly human-annotated, whose scales are usually limited due to their labor-intensive and time-consuming nature. Results: We construct BioRel, a large-scale dataset for biomedical relation extraction problem, by using Unified Medical Language System as knowledge base and Medline as corpus. We first identify mentions of entities in sentences of Medline and link them to Unified Medical Language System with Metamap. Then, we assign each sentence a relation label by using distant supervision. Finally, we adapt the state-of-the-art deep learning and statistical machine learning methods as baseline models and conduct comprehensive experiments on the BioRel dataset. Conclusions: Based on the extensive experimental results, we have shown that BioRel is a suitable large-scale datasets for biomedical relation extraction, which provides both reasonable baseline performance and many remaining challenges for both deep learning and statistical methods. Keywords: Distant supervision, Relation extraction, Information extraction, Medline
Backgrounds In recent years, we have witnessed a rapid growth in biomedical literature. Cohen and Hunter [1] provide an explanation on why the growth in PubMed and Medline publications is phenomenal. However, all the biomedical knowledge in these publications is expressed in the form of unstructured text, which cannot be easily utilized by computer programs. In fact, it is also very hard to manually transform all these knowledge in publications into structured form due to the large quantity of publications. Hence, automated text processing methods for transforming knowledge in text form into machine © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons lice
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