Challenges for ontology repositories and applications to biomedicine & agronomy

Clement Jonquet 1, 2
1 SMILE - Système Multi-agent, Interaction, Langage, Evolution
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The explosion of the number of ontologies and vocabularies available in the Semantic Web makes ontology libraries and repositories mandatory to find and use them. Their functionalities span from simple on-tology listing with more or less of metada-ta description to portals with advanced on-tology-based services: browse, search, vis-ualization, metrics, annotation, etc. Ontol-ogy libraries and repositories are usually developed to address certain needs and communities. BioPortal, the ontology repository built by the US National Center for Biomedical Ontologies BioPortal relies on a domain independent technology already reused in several projects from bio-medicine to agronomy and earth sciences. In this position paper, we describe six high level challenges for ontology repositories: metadata & selection, multilingualism, alignment, new generic ontology-based services, annotations & linked data, and interoperability & scalability. Then, we present some propositions to address these challenges and point to our previously published work and results obtained within applications –reusing NCBO technology– to biomedicine and agronomy in the context of the NCBO, SIFR and AgroPortal projects.
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Communication dans un congrès
Juan Antonio Lossio-Ventura; Hugo Alatrista-Salas. SIMBig: Symposium on Information Management and Big Data, Sep 2017, Lima, Peru. 4th Annual International Symposium on Information Management and Big Data, 2029, pp.25-37, 2017, 〈http://simbig.org/SIMBig2017/〉
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Clement Jonquet. Challenges for ontology repositories and applications to biomedicine & agronomy. Juan Antonio Lossio-Ventura; Hugo Alatrista-Salas. SIMBig: Symposium on Information Management and Big Data, Sep 2017, Lima, Peru. 4th Annual International Symposium on Information Management and Big Data, 2029, pp.25-37, 2017, 〈http://simbig.org/SIMBig2017/〉. 〈lirmm-01679500〉

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