Semantic Annotation Workflow using Bio-Ontologies

International audienceBiologists have adopted ontologies: *To provide canonical representation of scientific knowledge *To annotate experimental data to enable interpretation, comparison, and discovery across databases *To facilitate knowledge-based applications for decisionsupport, natural language processing, and data integration But off-the-shelf solutions for the biologist to use ontologies are rare (versions, format, availability, license, overlap, etc.) Automatically process complex biological resources text content and generate annotations : * Large-scale - to scale up to many resources and ontologies * Automatic - to keep precision and accuracy * Easy to use and to access - web service approach * Customizable - to fit very specific needs * Smart - to leverage the knowledge contained in ontologies There have been success stories to reproduce: GO annotations, PubMed indexing, etc

SIFR BioPortal : Un portail ouvert et générique d’ontologies et de terminologies biomédicales françaises au service de l’annotation sémantique

National audienceContexte – Le volume de données en biomédecine ne cesse de croître. En dépit d'une large adoption de l'anglais, une quantité significative de ces données est en français. Dans le do-maine de l’intégration de données, les terminologies et les ontologies jouent un rôle central pour structurer les données biomédicales et les rendre interopérables. Cependant, outre l'existence de nombreuses ressources en anglais, il y a beaucoup moins d'ontologies en français et il manque crucialement d'outils et de services pour les exploiter. Cette lacune contraste avec le montant considérable de données biomédicales produites en français, par-ticulièrement dans le monde clinique (e.g., dossiers médicaux électroniques). Methode & Résultats – Dans cet article, nous présentons certains résultats du projet In-dexation sémantique de ressources biomédicales francophones (SIFR), en particulier le SIFR BioPortal, une plateforme ouverte et générique pour l’hébergement d’ontologies et de terminologies biomédicales françaises, basée sur la technologie du National Center for Biomedical Ontology. Le portail facilite l’usage et la diffusion des ontologies du domaine en offrant un ensemble de services (recherche, alignements, métadonnées, versionnement, vi-sualisation, recommandation) y inclus pour l’annotation sémantique. En effet, le SIFR An-notator est un outil d’annotation basé sur les ontologies pour traiter des données textuelles en français. Une évaluation préliminaire, montre que le service web obtient des résultats équivalents à ceux reportés précedement, tout en étant public, fonctionnel et tourné vers les standards du web sémantique. Nous présentons également de nouvelles fonctionnalités pour les services à base d’ontologies pour l’anglais et le français

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

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

Representing NCBO Annotator results in standard RDF with the Annotation Ontology

International audienceSemantic annotation is part of the Semantic Web vision. The Annotation Ontology is a model that have been proposed to represent any annotations in standard RDF. The NCBO Annotator Web service is a broadly used service for annotations in the biomedical domain, offered within the BioPortal platform and giving access to more than 350+ ontologies. This paper presents a new output format to represent the NCBO Annotator results in RDF with the Annotation Ontology. We briefly present both technologies and describe the mappings to enable the representation. A Java library is available to parse the current JSON outputs to RDF/XML format. By rendering results in RDF, we make the annotations generated by the NCBO Annotator follow the Semantic Web standards making possible among other things to offer them as linked data

Patient's rationale: Patient Knowledge retrieval from health forums

International audienceOnline health forums are areas of exchange where patients, on condition of anonymity, can speak freely on their personal experiences. These resources are a gold mine for health professionals—giving them access to patient to patient, patient to health professional and even health professional to health professional exchanges. In this study, we used text mining techniques to analyse health forums in order to extract emotions (e.g., joy, anger, surprise, etc.) expressed by patients. After a study of real messages, we demonstrate the difficulty of manual annotation due to the low level of agreement between humans. We propose a method to identify the polarity of a message and extract one or several emotions. This method was validated on a substantial real dataset

Que ressentent les patients ?

International audienceLes forums de santé en ligne sont des espaces d'échanges où les patients partagent leurs sentiments à propos de leurs maladies, traitements, etc. Sous couvert d'anonymat, ils expriment très librement leurs expériences person-nelles. Ces forums sont donc une source d'informations très utile pour les pro-fessionnels de santé afin de mieux identifier et comprendre les problèmes, les comportements et les sentiments de leurs patients. Dans cet article, nous propo-sons d'exploiter les messages des forums via des techniques de fouille de textes pour extraire des traces d'émotions (e.g. joie, colère, surprise , etc.)