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PeerJ. 2018 Jan 2;6:e4201. doi: 10.7717/peerj.4201. eCollection 2018.

Biotea: semantics for Pubmed Central.

Author information

Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain.
Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Cali, Colombia.
Temporal Knowledge Bases Group, Department of Computer Languages and Systems, Universitat Jaume I, Castelló de la Plana, Spain.
Maastricht University, Institute of Data Science, Maastricht, The Netherlands.


A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Our datasets, methods and techniques are available at


Linked data; Ontology; RDF; SPARQL; Semantic; Semantic web

Conflict of interest statement

The authors declare there are no competing interests.

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