Un validateur d'ontologies par rapport à des profils OWL implémenté dans le langage STTL

National audienceDans cet article, nous abordons la question de recherche générale Comment exprimer des contraintes sur des données RDF et comment vérifier qu'un graphe RDF satisfasse un certain nombre de contraintes ? Nous nous concentrons sur le cas particulier de l'expression des contraintes telles que définies par les profils de OWL 2 et nous vérifions ces contraintes pour déterminer la conformité d'une ontologie OWL et mettre en évidence la présence éventuelle d'énoncés sources de non conformité. Nous proposons une approche basée sur le langage SPARQL Template Transformation Language (STTL). Un template STTL est une règle de transformation qui s'applique sur un graphe RDF donné et par le biais d'appels récursifs de templates STTL sur un graphe RDF nous obtenons une sortie textuelle, résultante de la transformation de ce même graphe. Nous montrons que STTL peut être utilisé comme un langage de contraintes sur RDF et nous l'utilisons afin d'implémenter la sémantique propre à chaque profil de OWL 2, chacun pouvant être interprété comme un ensemble de contraintes à respecter sur les définitions de classes et de propriétés. Chaque profil de OWL 2 est ainsi représenté par un ensemble de templates STTL qu'une ontologie valide se doit de satisfaire

Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction

International audienceAlthough there are many medical standard vocabularies available, it remains challenging to properly identify domain concepts in electronic medical records. Variations in the annotations of these texts in terms of coverage and abstraction may be due to the chosen annotation methods and the knowledge graphs, and may lead to very different performances in the automated processing of these annotations. We propose a semi-supervised approach based on DBpedia to extract medical subjects from EMRs and evaluate the impact of augmenting the features used to represent EMRs with these subjects in the task of predicting hospitalization. We compare the impact of subjects selected by experts vs. by machine learning methods through feature selection. Our approach was experimented on data from the database PRIMEGE PACA that contains more than 600,000 consultations carried out by 17 general practitioners (GPs)

Validating Ontologies against OWL 2 Profiles with the SPARQL Template Transformation Language

International audienceIn this paper we address the general research question of How can we express constraints on RDF data and how can we check that an RDF graph satisfies some given constraints? and we focus on expressing constraints defining OWL 2 profiles and checking these constraints for OWL validation. We propose an approach based on the SPARQL Template Transformation language (STTL). An STTL template is a transformation rule that applies to a given RDF graph and the recursive call of a set of STTL templates on an RDF graph outputs some textual data resulting from the transformation of this graph. We show that STTL can be used as a constraint language for RDF and we use it to implement the semantics of OWL 2 profiles: each profile is represented by a set of STTL templates that a valid ontology must satisfy

Enhancing domain-specific ontologies at ease

International audienceWe present a methodology to enhance domain-specific ontologies by (i) manual annotation of texts with the concepts in the domain ontology, (ii) matching annotated concepts with the closest YAGO-Wikipedia concept and (iii) using concepts from other ontologies that cover complementary domains. This method reduces the difficulty of aligning ontologies, because the alignment is carried out within the scope of an example. The resulting alignment is a partial connection between diverse ontologies, and also a strong connection to Linked Open Data. By aligning these ontologies, we are increasing the ontological coverage for texts in that domain. Moreover, by aligning domain ontologies to the Wikipedia (via YAGO) we can obtain manually annotated examples of some of the concepts, effectively populating the ontology with examples. We present two applications of this process in the legal domain. First, we annotate sentences of the European Court of Human Rights with the LKIF ontology, at the same time matching them with the YAGO ontology. Second, we annotate a corpus of customer questions and answers from an insurance web page with the OMG ontology for the insurance domain, matching it with the YAGO ontology and complementing it with a financial ontology

Évaluation des améliorations de prédiction d'hospitalisation par l'ajout de connaissances métier aux dossiers médicaux

National audienceThe knowledge available through electronic medical records (EMR) themselves remains limited by the fact the features used by a machine learning algorithms from a text alone do not contain all the implicit information known by a domain expert. We propose and evaluate the ontological augmentations of features extracted from textual information from EMRs on several machine learning algorithms to predict hospitalization.Les dossiers médicaux électroniques (DME) contiennent des informations essentielles sur les différents épisodes symptomatiques qu'un patient a subis. Cependant, les connaissances disponibles à travers ces enregistrements restent limitées : les attributs extractibles à partir de ces textes pour un algorithme d'apprentissage ne contiennent pas toutes les informations implicites connues par un expert. Afin d'évaluer et de pallier ce problème, nous avons étudié l'impact de l'augmentation des textes et des informations textuelles en provenance des DMEs par des annotations ontologiques générées automatiquement à partir de leur analyse afin d'enrichir en amont les représentations vectorielles utilisées ensuite par des algorithmes d'apprentissage

Bottom-up enrichment of top-down ontologies through annotation

International audienceWe present a methodology to enhance domain-specific ontologies by (i) addressing a manual annotation of texts with the concepts in the domain ontology, (ii) matching the annotated concepts with the closest YAGO-Wikipedia concept available, and (iii) using concepts from other ontologies that cover complementary domains. This method reduces the difficulty of aligning ontologies, because annotators are asked to associate two labels from different inventories to a concrete example, which requires a simple judgment. In a second phase, those correspondences are consolidated into a proper alignment. The resulting alignment is a partial connection between diverse ontologies, and also a strong connection to Linked Open Data. By aligning these ontologies, we are increasing the ontological coverage for texts in that domain. Moreover, by aligning domain on-tologies to the Wikipedia (via YAGO), we can obtain manually annotated examples for some of the concepts, effectively populating the ontology with examples. We present two applications of this process in the legal domain. First, we annotate sentences of the European Court of Human Rights with the LKIF ontology, at the same time matching them with the YAGO ontology. Second, we annotate a corpus of customer questions and answers from an insurance web page with the P&C ontology for the insurance domain, matching it with the YAGO ontology and complementing it with a financial ontology

Interface d'Aide à la Décision pour Prédire l'Hospitalisation de Patients et planifier les Actions Préventives pour Prévenir cet Événement

National audiencePhysicians are confronted with a constant increase in the number of their patients given the numerus clausus imposed on medical studies in France. In addition, the overall aging of the population requires them to treat patients with many diseases (comorbidity), which complicates the management of patients since polypharmacy implies the appearance of unexpected adverse drug’s effects. In this paper, we present the interface of an algorithm de-veloped to assist in the decision-making process of general practitioners (GPs) that allows them to identify in patients the first signs that lead to hospitalization and medical problems to be treated as a priorityLes médecins sont confrontés à une augmentation constante du nombre de leurs patients compte tenu du numerus clausus imposé aux études médicales en France. De plus, le vieillissement global de la population les oblige à traiter des patients atteints de nombreuses maladies chroniques (comorbidité), ce qui complique la prise en charge des patients puisque la polymédication implique l'apparition d'effets indésirables. Dans cet article, nous présentons l'interface d'un algorithme développé pour aider à la prise de décision des médecins généralistes qui leur permet d'identifier les premiers signes qui mènent les patients à l'hospitalisation et les problèmes médicaux à traiter en priorité

Covid-on-the-Web: Knowledge Graph and Services to Advance COVID-19 Research

International audienceScientists are harnessing their multidisciplinary expertise and resources to fight the COVID-19 pandemic. Aligned with this mind-set, the Covid-on-the-Web project aims to allow biomedical researchers to access, query and make sense of COVID-19 related literature. To do so, it adapts, combines and extends tools to process, analyze and enrich the "COVID-19 Open Research Dataset" (CORD-19) that gathers 50,000+ full-text scientific articles related to the coronaviruses. We report on the RDF dataset and software resources produced in this project by leveraging skills in knowledge representation, text, data and argument mining, as well as data visualization and exploration. The dataset comprises two main knowledge graphs describing (1) named entities mentioned in the CORD-19 corpus and linked to DBpedia, Wikidata and other BioPortal vocabularies, and (2) arguments extracted using ACTA, a tool automating the extraction and visualization of argumentative graphs, meant to help clinicians analyze clinical trials and make decisions. On top of this dataset, we provide several visualization and exploration tools based on the Corese Semantic Web platform, MGExplorer visualization library, as well as the Jupyter Notebook technology. All along this initiative, we have been engaged in discussions with healthcare and medical research institutes to align our approach with the actual needs of the biomedical community, and we have paid particular attention to comply with the open and reproducible science goals, and the FAIR principles

Logistic Ontologies Landscape: Challenges, Gaps, and Opportunities for Improved Representation

Data standards are essential for coordinating logistics information across diverse stakeholders. This report explores the role of ontologies in shaping logistics data structures and enhancing interoperability. By assessing current logistics ontologies, it identifies challenges and gaps in data representation, aiming to describe the current state of logistics data standards.Funding: Vinnova 2021-03653</p

Logistic Ontologies Landscape: Challenges, Gaps, and Opportunities for Improved Representation

Data standards are essential for coordinating logistics information across diverse stakeholders. This report explores the role of ontologies in shaping logistics data structures and enhancing interoperability. By assessing current logistics ontologies, it identifies challenges and gaps in data representation, aiming to describe the current state of logistics data standards.Funding: Vinnova 2021-03653</p