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Rich metadata is required for a comprehensive description of research assets. We developed a metadata schema for clinical and epidemiological research studies based on existing generic and domain-specific metadata vocabularies. It forms the basis for various search and data management services. Interoperability remains a challenge, as various health research standards are to be supported in the medium term. At the same time, embedding our infrastructure in national and international initiatives requires the use of overarching syntactic and semantic standards and vocabularies. In this paper we present a prototypical implementation in CEDAR Workbench. This not only provides a graphical web interface for collaboration and a possibility for form-based data entry for testing purposes. CEDAR also enables the use of standard vocabularies, annotation of concepts with medical terminologies, and a serialization in an RDF-JSON format.

NATURA 2000 je europska ekološka mreža, osmišljena na osnovu Direktive o pticama i Direktive o staništima. Za cilj ima zaštitu vrsta i staništa koja su ključna za održavanje prirodne ravnoteže na prostorima Europske Unije. U ovom radu je dan pregledni prikaz i sistematizacija bentoskih stanišnih tipova ekološke mreže NATURA 2000 u hrvatskim teritorijalnim vodama Jadranskog mora. Temeljem rezultata detaljne analize podataka nacionalnog Informacijskog sustava zaštite prirode (Bioportal) i njihove obrade primjenom Geografskog informacijskog sustava QGIS 3.20.2. izvršena je sistematizacija 226 NATURA 2000 područja s obzirom na: tip bentoskih staništa i njihov i geografski položaj, regionalnu pripadnost (sjeverni, srednji, južni Jadran), mjere zaštite (PPOVS i POVS), pritiske, ukupnu površinu zaštićenih područja s postotnim udjelom morske površine, te površine ukupnog broja 496 bentoskih staništa NATURA 2000, na ukupnoj površini od 4508 km2. Prikazani su sumarni rezultati i komparativni rezultati za područja sjevernog, srednjeg i južnog Jadrana. Kartirana je temeljna geografska raspodjela bentoskih područja ekološke mreže NATURA 2000 u Jadranu. NATURA 2000 is a European ecological network, designed on the basis of the Birds Directive and the Habitats Directive. Its goal is the protection of species and habitats that are essential for maintaining the natural balance in the European Union. This paper provides an overview and systematization of the benthic habitat types of the ecological network NATURA 2000 in the Croatian territorial waters of the Adriatic Sea. Based on the results of a detailed analysis of the data of the National Nature Protection Information System (Bioportal) and their processing using the Geographical Information System QGIS 3.20.2. 226 NATURA 2000 areas were systematized with regard to: type of benthic habitats and their geographic location, regional affiliation (northern, middle, southern Adriatic), protection measures (PPOVS and POVS), pressures, total area of protected areas with percentage of sea surface, and the surface of the total number of 496 NATURA 2000 benthic habitats, on a total surface of 4508 km2. Summary results and comparative results for the areas of the northern, central and southern Adriatic are presented. The basic geographical distribution of the benthic areas of the NATURA 2000 ecological network in the Adriatic was mapped

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Background Following COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition. Objective This study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not. Methods We used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics–defined LC symptoms between groups. We used descriptive statistics and logistic regression. Results The long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P Conclusions Our LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.

Ekološka mreža Natura 2000 najveća je mreža zaštićenih područja u svijetu. Pravni okvir za njezino očuvanje sadržano je u Direktivi o pticama i Direktivi o staništima, koje provode sve države članice EU. Natura 2000 područja se dijele na Područja značajna za ptice (POP) i područja značajna za vrste i stanišne tipove (POVS). Ovim radom dan je uvid i pregled Natura 2000 područja u Istarskoj županiji temeljem analize podataka s Bioportala. Prikazane su sve ciljne vrste unutar ciljnih staništa te su sistematizirane prijetnje i pritisci. Dodatno, podaci Natura 2000 područja u Istri uspoređeni su s područjima Ekološke mreže na poluotoku Pelješcu u cilju komparacije njihovih Natura 2000 područja (POVS i POP). Podaci s Bioportala obrađeni su u programu Excel, gdje se radila usporedba postotne prostorne zastupljenosti tipova područja (POP i POVS), na dva najveća hrvatska poluotoka. Istra ima vrlo bogatu biološku raznolikost s izraženijim pritiscima i prijetnjama, u odnosu na Pelješac. The Natura 2000 ecological network is the largest network of protected areas in the world. The legal framework for its conservation is contained in the Birds Directive and the Habitats Directive, which are implemented by all EU Member States. Natura 2000 sites are divided into Areas of Importance for Birds (POP) and Areas of Importance for Species and Habitat Types (POVS). This paper provides an insight and overview of Natura 2000 sites in the County of Istria based on the analysis of data from the Bioportal. All target species within the target habitats are presented and threats and pressures are systematized. In addition, data from Natura 2000 sites in Istria were compared with the areas of the Ecological Network on the Pelješac Peninsula in order to compare their Natura 2000 sites (POVS and POP). Data from the Bioportal were processed in Excel, where a comparison of the percentage of spatial representation of area types (POP and POVS) was made on the two largest Croatian peninsulas. Istria has a very rich biological diversity with more pronounced pressures and threats, compared to Peljesac.

International audience; 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.

Comparison of resources is a frequent task in different bio-informatics applications, including drug-target interaction, drug repositioning and mechanism of action understanding, among others. This paper proposes a general method for the logical comparison of resources modeled in Resource Description Framework and shows its distinguishing features with reference to the comparison of drugs. In particular, the method returns a description of the commonalities between resources, rather than a numerical value estimating their similarity and/or relatedness. The approach is domain-independent and may be flexibly adapted to heterogeneous use cases, according to a process for setting parameters which is completely explicit. The paper also presents an experiment using the dataset Bioportal as knowledge source; the experiment is fully reproducible, thanks to the elicitation of criteria and values for parameter customization.

The NCBO BioPortal provides Web services for over 500 biomedical ontologies, allowing investigators to annotate and retrieve data, generate value sets, and perform advanced analytics of a wide range of biomedical and clinical data. BioPortal provides core services for many CEDAR metadata activities, and also heavily serves the wider BD2K community—seven out of eight centers responding to the BD2K metadata survey cited BioPortal as an essential resource for their work. We have completed some long-planned features enabling or optimizing CEDAR’s use of BioPortal. With this year’s supplement, we have begun pursuing enhancements to maximize BioPortal’s value to CEDAR and advance BioPortal’s integration with other BD2K programs and services. We present the work accomplished, and enhancements that are being made available over the course of this supplement. The first set of completed features advanced CEDAR‘s usage of BioPortal concepts and value sets. We made subtle improvements to BioPortal’s ontology presentation services and added features for accessing and extending value set services. We also began enhancing BioPortal’s term-specific services for use by CEDAR and others. We have prototyped a concept-centric view of ontology data, providing for a given term all significant information available across all BioPortal ontologies as well as other relevant sources. Using this baseline, we plan to provide a “best-term” identification service, taking into account more contextual information specific to term discovery; and a query-expansion service. As CEDAR adds property relations for its fields and terms, we will advance BioPortal’s handling of ontology properties (the formal relationships between terms), extending the BioPortal API to better handle and retrieve ontology properties. We’ll also be increasing BioPortal’s metadata-handling capabilities. Finally, we intend to harden BioPortal’s capacity for complex queries by CEDAR, both by optimizing query handling (partially completed), and developing application-level test suites around CEDAR’s most important queries.

National audience; Scientific communities are using an increasing number of ontologies. Repositories make them available, like the NCBO BioPortal which currently hosts more than 500 biomedical ontologies. Now the question is how to find the ontology we need? One solution is to describe each ontology with appropriate metadata. However, none of the existing metadata vocabularies can completely meet this need if taken independently. We have reviewed a large number of vocabularies, such as Dublin Core, OMV, DCAT, or VOID, as well as the properties implemented by common ontology repositories. We then listed those properties into a simplified model of 124 properties. We present a few examples of use of these properties within the AgroPortal, an ontology repository for agronomy, and explain how the portal handles these properties to facilitate ontology description and selection.; Les communautés scientifiques utilisent un nombre croissant d'ontologies. Pour les mettre à disposition, il existe des portails d'ontologies, à l'exemple du NCBO BioPortal qui regroupe actuellement plus de 500 ontologies biomédicales. Mais face à cette avalanche de ressources, comment trouver l'ontologie qui répondra à nos besoins ? Une solution consiste à décrire chaque ontologie avec des métadonnées appropriées. Or, il n'existe pas à ce jour de vocabulaire de métadonnées suffisamment exhaustif pour répondre à ce besoin. Nous avons passé en revue un grand nombre de vocabulaires, tels que Dublin Core, OMV, DCAT ou VOID ainsi que les propriétés implémentées par les portails d'ontologies les plus courant. Nous en avons produit un modèle simplifié composé de 124 propriétés. Nous présentons ici quelques exemples d'utilisation de ces propriétés dans AgroPortal, un portail d'ontologies dédié à l'agronomie, et nous expliquons comment elles sont gérées et utilisées pour la description et l'identification d'ontologies.

National audience; Contexte – 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.

International audience; Even if multilingual ontologies are now more common, for historical reasons, in the biomedical domain, many ontologies or terminologies have been translated from one natural language to another resulting in two potentially aligned ontologies but with their own specificity (e.g., format, developers, and versions). Most often, there is no formal representation of the translation links between translated ontologies and original ones and those mappings are not formally available as linked data. However, these mappings are very important for the interoperability and the integration of multilingual biomedical data. In this paper, we propose an approach to represent translation mappings between ontologies based on the NCBO BioPortal format. We have reconciled more than 228K mappings between ten English ontologies hosted on NCBO BioPortal and their French translations. Then, we have stored both the translated ontologies and mappings on a French customized version of the platform, called the SIFR BioPortal, making the whole thing available in RDF. Reconciling the mappings turned more complex than expected because the translations are rarely exactly the same than the original ontologies as discussed in this paper.

International audience; De nos jours, les ontologies sont souvent développées de manière multilingue. Cependant, pour des raisons historiques, dans le domaine biomédical, de nombreuses ontologies ou terminologies ont été traduites d'une langue à une autre ou sont maintenues explicitement dans chaque langue. Cela génère deux ontologies potentiellement alignées mais avec leurs propres spécificités (format, développeurs, versions, etc.). Souvent, il n'existe pas de représentation formelle des liens de traduction reliant les ontologies traduites aux originales et ils ne sont pas accessibles sous forme de linked data. Cependant, ces liens sont très importants pour l'interopérabilité et l'intégration de données biomédicales multilingues. Dans cet article, nous présentons les résultats d'une étude de réconciliation des liens de traduction entre ontologies sous forme d'alignements multilingues. Nous avons réconcilié et représenté à l'aide de vocabulaire du web sémantique, plus de 228K mappings entre dix ontologies anglaises hébergées sur le NCBO BioPortal et leurs traductions françaises. Ensuite, nous avons stocké à la fois les ontologies et les mappings sur une version française de la plate-forme, appelée SIFR BioPortal, pour rendre le tout disponible en RDF (données liées). La réconciliation des alignements s'est avérée plus complexe que ce qu'on pourrait penser car les traductions ne sont que rarement l'exacte copie des originales comme nous le discutons. Mots-clés : Mappings ou alignements multilingues, réconciliations de mappings, web sémantique, données liées, alignement d'ontologies, entrepôt d'ontologies, ontologies biomédicales, BioPortal, interopérabilité et intégration de données, diffusion de données et de connaissances.

International audience; This paper presents a novel background knowledge approach which selects and combines existing mappings from a given biomedical ontology repository to improve ontology alignment. Current background knowledge approaches usually select either manually or automatically a limited number of different ontologies and use them as a whole for background knowledge. Whereas in our approach, we propose to pick up only relevant concepts and relevant existing mappings linking these concepts all together in a specific and customized background knowledge graph. Paths within this graph will help to discover new mappings. We have implemented and evaluated our approach using the content of the NCBO BioPortal repository and the Anatomy benchmark from the Ontology Alignment Evaluation Initiative. We used the mapping gain measure to assess how much our final background knowledge graph improves results of state-of-the-art alignment systems. Furthermore, the evaluation shows that our approach produces a high quality alignment and discovers map-pings that have not been found by state-of-the-art systems.

International audience; Semantically analyze patient-generated text from a biomedical perspective is challenging because of the vocabulary gap between patients and health professionals. The medical expertise and vocabulary is well formalized in standards terminologies and ontologies, which enable semantic analysis of expert-generated text; however resources which formalize the vocabulary of health consumers (patients and their family, laypersons in general) remain scarce. The situation is even worse if one is interested in another language than English. In previous studies, we attempted to produce a French preliminary Consumer Health Vocabulary (CHV) by mining the language used within online public forums & Facebook groups about breast cancer. In this work, we show our effort to concretely align the vocabulary produced to standard terminologies and to represent its content (terms & mappings) using semantic web languages such as RDF and SKOS. We used a sample of 173 relations built around 64 expert concepts which have been automatically (89%) or manually (11%) aligned to standard biomedical terminologies, in our case: MeSH, MedDRA and SNOMEDint. The resulting vocabulary, called MuEVo (Multi-Expertise Vocabulary) and the mappings are publicly available in the SIFR BioPortal French biomedical ontology repository.

Data generated from cancer nanotechnology research are so diverse and large in volume that it is difficult to share and efficiently use them without informatics tools. In particular, ontologies that provide a unifying knowledge framework for annotating the data are required to facilitate the semantic integration, knowledge-based searching, unambiguous interpretation, mining and inferencing of the data using informatics methods. In this paper, we discuss the design and development of NanoParticle Ontology (NPO), which is developed within the framework of the Basic Formal Ontology (BFO), and implemented in the Ontology Web Language (OWL) using well-defined ontology design principles. The NPO was developed to represent knowledge underlying the preparation, chemical composition, and characterization of nanomaterials involved in cancer research. Public releases of the NPO are available through BioPortal website, maintained by the National Center for Biomedical Ontology. Mechanisms for editorial and governance processes are being developed for the maintenance, review, and growth of the NPO.

International audience; Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural language processing and decision support. BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides access via Web services and Web browsers to ontologies developed in OWL, RDF, OBO format and Protégé frames. BioPortal functionality includes the ability to browse, search and visualize ontologies. The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content by providing features to add notes to ontology terms, mappings between terms and ontology reviews based on criteria such as usability, domain coverage, quality of content, and documentation and support. BioPortal also enables integrated search of biomedical data resources such as the Gene Expression Omnibus (GEO), ClinicalTrials.gov, and ArrayExpress, through the annotation and indexing of these resources with ontologies in BioPortal. Thus, BioPortal not only provides investigators, clinicians, and developers ‘one-stop shopping' to programmatically access biomedical ontologies, but also provides support to integrate data from a variety of biomedical resources.

Proceedings of the Fourth Workshop on the Multilingual Semantic Web (MSW4)co-located with 12th Extended Semantic Web Conference (ESWC 2015); International audience; Ontology indexes and repositories are important in the realization of the Semantic Web; however, the need has clearly moved to multilingual capabilities that are hard to offer when dealing with multiple ontologies, originally in different formats and contributed by an open community. In this paper, we present a roadmap for addressing the issues of dealing with multilingual or monolingual ontologies in BioPortal, the reference ontology repository in biomedicine, currently mostly English-oriented. We propose a set of representations to support multilingualism in the portal and to enable a complete use of the functionalities and services for any kind of ontologies and data. While encouraging the community to use the best available specifications to represent multilingual content e.g., Lemon; our objective is to handle multilingualism in a proper semantically rich and consistent manner in the ontology repository. We are currently deploying and implementing these representations in a local instance of BioPortal for French ontologies.

Du 01 Mars 2013 au 31 aout 2013, j'ai effectué mon stage de Master 2 DECOL au sein du LIRMM, à Montpellier. Ce stage de Master 2 fut l'occasion de mettre en application les nombreuses connaissances enseignées en Master DECOL. Ce stage s'inscrit dans le cadre du projet SIFR financé par l'Agence Nationale de la Recherche (ANR) 3.Dans ce chapitre nous allons présenter le projet dans lequel s'inscrit ce stage et les différents partenaires du stage. Nous détaillerons également les objectifs et les technologies utilisées.

As new biomedical technologies are developed, the amount of publically available biomedical data continues to increase. To help manage these vast and disparate data sources, researchers have turned to the Semantic Web. Specifically, ontologies are used in data annotation, natural language processing, information retrieval, clinical decision support, and data integration tasks. The development of software applications to perform these tasks requires the integration of Web services to incorporate the wide variety of ontologies used in the health care and life sciences. The National Center for Biomedical Ontology, a National Center for Biomedical Computing created under the NIH Roadmap, developed BioPortal, which provides access to one of the largest repositories of biomedical ontologies. The NCBO Web services provide programmtic access to these ontologies and can be grouped into four categories; Ontology, Mapping, Annotation, and Data Access. The Ontology Web services provide access to ontologies, their metadata, ontology versions, downloads, navigation of the class hierarchy (parents, children, siblings) and details of each term. The Mapping Web services provide access to the millions of ontology mappings published in BioPortal. The NCBO Annotator Web service “tags” text automatically with terms from ontologies in BioPortal, and the NCBO Resource Ind ex Web services provides access to an ontology-based index of public, online data resources. The NCBO Widgets package the Ontology Web services for use directly in Web sites. The functionality of the NCBO Web services and widgets are incorporated into semantically aware applications for ontology development and visualization, data annotation, and data integration. This overview will describe these classes of applications, discuss a few examples of each type, and which NCBO Web services are used by these applications.

International audience; The field of biomedicine has embraced the Semantic Web probably more than any other field. As a result, there is a large number of biomedical ontologies covering overlapping areas of the field. We have developed BioPortal — an open community-based repository of biomedical ontologies. We analyzed ontologies and terminologies in BioPortal and the Unified Medical Language System (UMLS), creating more than 4 million mappings between concepts in these ontologies and terminologies based on the lexical similarity of concept names and synonyms. We then analyzed the mappings and what they tell us about the ontologies themselves, the structure of the ontology repository, and the ways in which the mappings can help in the process of ontology design and evaluation. For example, we can use the mappings to guide users who are new to a field to the most pertinent ontologies in that field, to identify areas of the domain that are not covered sufficiently by the ontologies in the repository, and to identify which ontologies will serve well as background knowledge in domain-specific tools. While we used a specific (but large) ontology repository for the study, we believe that the lessons we learned about the value of a large-scale set of mappings to ontology users and developers are general and apply in many other domains.