Semantic annotation in biomedicine: the current landscape

• Published in: Journal of biomedical semantics Vol. 8; no. 1; pp. 44 - 18
• Main Authors: Jovanović, Jelena, Bagheri, Ebrahim
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 22.09.2017; BioMed Central; BMC
• Subjects: Abundance; Access to information; Annotations; Bioinformatics; Biological Ontologies; Biomedical ontologies; Biomedical Research; Biomedical text mining; Customization; Data mining; Disease; Electronic Health Records; Evidence-based medicine; Genes; Humans; Infections; Information Storage and Retrieval; Knowledge; Knowledge bases (artificial intelligence); Laboratories; Linked Data; Multilingualism; Natural language processing; Natural language processing (NLP); Ontology; Open data; Proteins; Review; Semantic annotation; Semantic technologies; Semantics; Software; Texts; Biomedical text mining; Natural language processing (NLP); Semantic annotation; Semantic technologies; Biomedical ontologies
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-017-0153-x

The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.