Semantic annotation and summarization of biomedical text

Converting Biomedical Text Annotated Resources into FAIR Research Objects with an Open Science Platform

Applied Sciences ◽

10.3390/app11209648 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9648

Author(s):

Alexandros Kanterakis ◽

Nikos Kanakaris ◽

Manos Koutoulakis ◽

Konstantina Pitianou ◽

Nikos Karacapilidis ◽

...

Keyword(s):

Web Services ◽

Open Source ◽

Semantic Annotation ◽

Workflow Management ◽

Open Science ◽

Free Access ◽

Biomedical Text ◽

Text Annotation ◽

Text Corpora ◽

Research Objects

Today, there are excellent resources for the semantic annotation of biomedical text. These resources span from ontologies, tools for NLP, annotators, and web services. Most of these are available either in the form of open source components (i.e., MetaMap) or as web services that offer free access (i.e., Whatizit). In order to use these resources in automatic text annotation pipelines, researchers face significant technical challenges. For open-source tools, the challenges include the setting up of the computational environment, the resolution of dependencies, as well as the compilation and installation of the software. For web services, the challenge is implementing clients to undertake communication with the respective web APIs. Even resources that are available as Docker containers (i.e., NCBO annotator) require significant technical skills for installation and setup. This work deals with the task of creating ready-to-install and run Research Objects (ROs) for a large collection of components in biomedical text analysis. These components include (a) tools such as cTAKES, NOBLE Coder, MetaMap, NCBO annotator, BeCAS, and Neji; (b) ontologies from BioPortal, NCBI BioSystems, and Open Biomedical Ontologies; and (c) text corpora such as BC4GO, Mantra Gold Standard Corpus, and the COVID-19 Open Research Dataset. We make these resources available in OpenBio.eu, an open-science RO repository and workflow management system. All ROs can be searched, shared, edited, downloaded, commented on, and rated. We also demonstrate how one can easily connect these ROs to form a large variety of text annotation pipelines.

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