Entity linking for biomedical literature

BMC Medical Informatics and Decision Making

Research Article

Daniel Howsmon¹  Juergen Hahn²  Boliang Zhang³  Heng Ji³  Deborah McGuinness⁴  James Hendler⁴  Jin G Zheng⁴ 

[1] Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, 110 8th Street, 12180, Troy, NY, USA;Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, 110 8th Street, 12180, Troy, NY, USA;Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th Street, 12180, Troy, NY, USA;Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, 12180, Troy, NY, USA;Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, 12180, Troy, NY, USA;Tetherless World Constellation, Rensselaer Polytechnic Institute, 110 8th Street, 12180, Troy, NY, USA;

关键词: semantic web;    biological ontologies;    text mining;    signal transduction;    wikification;    entity linking;    biomedical literature;   

DOI  :  10.1186/1472-6947-15-S1-S4

来源: Springer

【 摘 要 】

BackgroundThe Entity Linking (EL) task links entity mentions from an unstructured document to entities in a knowledge base. Although this problem is well-studied in news and social media, this problem has not received much attention in the life science domain. One outcome of tackling the EL problem in the life sciences domain is to enable scientists to build computational models of biological processes with more efficiency. However, simply applying a news-trained entity linker produces inadequate results.MethodsSince existing supervised approaches require a large amount of manually-labeled training data, which is currently unavailable for the life science domain, we propose a novel unsupervised collective inference approach to link entities from unstructured full texts of biomedical literature to 300 ontologies. The approach leverages the rich semantic information and structures in ontologies for similarity computation and entity ranking.ResultsWithout using any manual annotation, our approach significantly outperforms state-of-the-art supervised EL method (9% absolute gain in linking accuracy). Furthermore, the state-of-the-art supervised EL method requires 15,000 manually annotated entity mentions for training. These promising results establish a benchmark for the EL task in the life science domain. We also provide in depth analysis and discussion on both challenges and opportunities on automatic knowledge enrichment for scientific literature.ConclusionsIn this paper, we propose a novel unsupervised collective inference approach to address the EL problem in a new domain. We show that our unsupervised approach is able to outperform a current state-of-the-art supervised approach that has been trained with a large amount of manually labeled data. Life science presents an underrepresented domain for applying EL techniques. By providing a small benchmark data set and identifying opportunities, we hope to stimulate discussions across natural language processing and bioinformatics and motivate others to develop techniques for this largely untapped domain.

【 授权许可】

Unknown   
© Zheng et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.