title: Entity linking for biomedical literature.
abstract: BACKGROUND
  The 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.
  
  
  METHODS
  Since 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.
  
  
  RESULTS
  Without 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.
  
  
  CONCLUSIONS
  In 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.
authors.forename: Jin G
authors.initials: JG
authors.lastname: Zheng
authors.forename: Daniel
authors.initials: D
authors.lastname: Howsmon
authors.forename: Boliang
authors.initials: B
authors.lastname: Zhang
authors.forename: Juergen
authors.initials: J
authors.lastname: Hahn
authors.forename: Deborah
authors.initials: D
authors.lastname: McGuinness
authors.forename: James
authors.initials: J
authors.lastname: Hendler
authors.forename: Heng
authors.initials: H
authors.lastname: Ji
mesh_terms: D057225:Data Mining / Q000379:methods*; D008490:Medical Informatics / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics; D015398:Signal Transduction