NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.Comment: 29 pages, 8 figures, 11 table

Ontology Repositories and Semantic Artefact Catalogues with the OntoPortal Technology

Dans tous les domaines de la science, de nombreuses ontologies (ou plus largement des artefacts sémantiques 1 ) sont utilisées pour représenter et annoter les données de manière standardisée. Les artefacts sémantiques sont devenus un élément maître pour atteindre les principes FAIR en matière de données

Ontology Repositories and Semantic Artefact Catalogues with the OntoPortal Technology

Il y a une explosion dans le nombre d’ontologies et d’artefacts sémantiques. Cet article traite de la nécessité pour les plateformes communes de recevoir, héberger, servir, aligner et activer leur réutilisation. Les catalogues sont nécessaires pour répondre à ce besoin et faire des ontologies FAIR (Findable, Accessible, interopérable et réutilisable)

Scoring semantic annotations returned by the NCBO Annotator

International audienceSemantic annotation using biomedical ontologies is required to enable data integration, interoperability, indexing and mining of biomedical data. When used to support semantic indexing the scoring and ranking of annotations become as important as provenance and metadata on the annotations themselves. In the biomedical domain, one broadly used service for annotations is the NCBO Annotator Web service, offered within the BioPortal platform and giving access to more than 350+ ontologies or terminologies. This paper presents a new scoring method for the NCBO Annotator allowing to rank the annotation results and enabling to use such scores for better indexing of the annotated data. By using a natural language processing-based term extraction measure, C-Value, we are able to enhance the original scoring algorithm which uses basic frequencies of the matches and in addition to positively discriminate multi-words term annotations. We show results obtained by comparing three different methods with a reference corpus of PubMed-MeSH manual annotations

Scoring semantic annotations returned by the NCBO Annotator

International audienceSemantic annotation using biomedical ontologies is required to enable data integration, interoperability, indexing and mining of biomedical data. When used to support semantic indexing the scoring and ranking of annotations become as important as provenance and metadata on the annotations themselves. In the biomedical domain, one broadly used service for annotations is the NCBO Annotator Web service, offered within the BioPortal platform and giving access to more than 350+ ontologies or terminologies. This paper presents a new scoring method for the NCBO Annotator allowing to rank the annotation results and enabling to use such scores for better indexing of the annotated data. By using a natural language processing-based term extraction measure, C-Value, we are able to enhance the original scoring algorithm which uses basic frequencies of the matches and in addition to positively discriminate multi-words term annotations. We show results obtained by comparing three different methods with a reference corpus of PubMed-MeSH manual annotations

Scoring semantic annotations returned by the NCBO Annotator

Abstract. Semantic annotation using biomedical ontologies is required to enable data integration, interoperability, indexing and mining of biomedical data. When used to support semantic indexing the scoring and ranking of annotations become as important as provenance and metadata on the annotations themselves. In the biomedical domain, one broadly used service for annotations is the NCBO Annotator Web service, offered within the BioPortal platform and giving access to more than 350+ ontologies or terminologies. This paper presents a new scoring method for the NCBO Annotator allowing to rank the annotation results and enabling to use such scores for better indexing of the annotated data. By using a natural language processing-based term extraction measure, C-Value, we are able to enhance the original scoring algorithm which uses basic frequencies of the matches and in addition to positively discriminate multi-words term annotations. We show results obtained by comparing three different methods with a reference corpus of PubMed-MeSH manual annotations

AgroPortal: a vocabulary and ontology repository for agronomy

Many vocabularies and ontologies are produced to represent and annotate agronomic data. However, those ontologies are spread out, in different formats, of different size, with different structures and from overlapping domains. Therefore, there is need for a common platform to receive and host them, align them, and enabling their use in agro-informatics applications. By reusing the National Center for Biomedical Ontologies (NCBO) BioPortal technology, we have designed AgroPortal, an ontology repository for the agronomy domain. The AgroPortal project re-uses the biomedical domain’s semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences. We offer a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web. The AgroPortal specifically satisfies requirements of the agronomy community in terms of ontology formats (e.g., SKOS vocabularies and trait dictionaries) and supported features (offering detailed metadata and advanced annotation capabilities). In this paper, we present our platform’s content and features, including the additions to the original technology, as well as preliminary outputs of five driving agronomic use cases that participated in the design and orientation of the project to anchor it in the community. By building on the experience and existing technology acquired from the biomedical domain, we can present in AgroPortal a robust and feature-rich repository of great value for the agronomic domain. Keyword