416 research outputs found
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
A domain ontology for the non-coding RNA field
Identification of non-coding RNAs (ncRNAs) has been significantly enhanced due to the rapid advancement in sequencing technologies. On the other hand, semantic annotation of ncRNA data lag behind their identification, and there is a great need to effectively integrate discovery from relevant communities. To this end, the Non-Coding RNA Ontology (NCRO) is being developed to provide a precisely defined ncRNA controlled vocabulary, which can fill a specific and highly needed niche
in unification of ncRNA biology
The Non-Coding RNA Ontology : a comprehensive resource for the unification of non-coding RNA biology
In recent years, sequencing technologies have enabled the identification of a wide range of non-coding RNAs (ncRNAs). Unfortunately, annotation and integration of ncRNA data has lagged behind their identification. Given the large quantity of information being obtained in this area, there emerges an urgent need to integrate what is being discovered by a broad range of relevant communities. To this end, the Non-Coding RNA Ontology (NCRO) is being developed to provide a systematically structured and precisely defined controlled vocabulary for the domain of ncRNAs, thereby facilitating the discovery, curation, analysis, exchange, and reasoning of data about structures of ncRNAs, their molecular and cellular functions, and their impacts upon phenotypes. The goal of NCRO is to serve as a common resource for annotations of diverse research in a way that will significantly enhance integrative and
comparative analysis of the myriad resources currently housed in disparate sources. It is our belief that the NCRO ontology can perform an important role in the comprehensive unification of ncRNA biology and, indeed, fill a critical gap in both the Open Biological and Biomedical Ontologies (OBO) Library and the National Center for Biomedical Ontology (NCBO) BioPortal. Our initial focus is on the ontological representation of small regulatory ncRNAs, which we see as the first step in providing a resource for the annotation of data about all forms of ncRNAs. The NCRO ontology
is free and open to all users
OntoCAT - a simpler way to access ontology resources
OntoCAT is an open source package developed to simplify the task of querying heterogeneous ontology resources. It supports local ontologies in OBO and OWL format as well as public repositories NCBO BioPortal and EBI Ontology Lookup Service (OLS). It is available from "http://ontocat.sourceforge.net":http://ontocat.sourceforge.ne
AgroPortal : a proposition for ontology-based services in the agronomic domain
Our project is to develop and support a reference ontology repository for the agronomic domain. By reusing the NCBO BioPortal technology, we have already designed and implemented a prototype ontology repository for plants and a few crops. We plan to turn that prototype into a real service to the community. The AgroPortal project aims at reusing the scientific outcomes and experience of the biomedical domain in the context of plant, agronomic and environment sciences. We will offer an ontology portal which features ontology hosting, search, versioning, visualization, comment, but we will also offer services for semantically annotating data with the ontologies, as well as storing and exploiting ontology alignments and data annotations. All of these within a fully semantic web compliant infrastructure. The main objective of this project is to enable straightforward use of agronomic related ontologies, avoiding data managers and researchers the burden to deal with complex knowledge engineering issues to annotate the research data. The AgroPortal project will specifically pay attention to respect the requirements of the agronomic community and the specificities of the crop domain. We will first focus on the outputs of a few existing driving agronomic use cases related to rice and wheat, with the goal of generalizing to other Crop Ontology related use cases. AgroPortal will offer a robust and stable platform that we anticipate will be highly valued by the community
Harnessing the power of unified metadata in an ontology repository: The case of AgroPortal
As any resources, ontologies, thesaurus, vocabularies and terminologies need to be described with relevant metadata to facilitate their identification, selection and reuse. For ontologies to be FAIR, there is a need for metadata authoring guidelines and for harmonization of existing metadata vocabularies—taken independently none of them can completely describe an ontology. Ontology libraries and repositories also have to play an important role. Indeed, some metadata properties are intrinsic to the ontology (name, license, description); other information, such as community feedbacks or relations to other ontologies are typically information that an ontology library shall capture, populate and consolidate to facilitate the processes of identifying and selecting the right ontology(ies) to use. We have studied ontology metadata practices by: (1) analyzing metadata annotations of 805 ontologies; (2) reviewing the most standard and relevant vocabularies (23 totals) currently available to describe metadata for ontologies (such as Dublin Core, Ontology Metadata Vocabulary, VoID, etc.); (3) comparing different metadata implementation in multiple ontology libraries or repositories. We have then built a new metadata model for our AgroPortal vocabulary and ontology repository, a platform dedicated to agronomy based on the NCBO BioPortal technology. AgroPortal now recognizes 346 properties from existing metadata vocabularies that could be used to describe different aspects of ontologies: intrinsic descriptions, people, date, relations, content, metrics, community, administration, and access. We use them to populate an internal model of 127 properties implemented in the portal and harmonized for all the ontologies. We—and AgroPortal's users—have spent a significant amount of time to edit and curate the metadata of the ontologies to offer a better synthetized and harmonized information and enable new ontology identification features. Our goal was also to facilitate the comprehension of the agronomical ontology landscape by displaying diagrams and charts about all the ontologies on the portal. We have evaluated our work with a user appreciation survey which confirms the new features are indeed relevant and helpful to ease the processes of identification and selection of ontologies. This paper presents how to harness the potential of a complete and unified metadata model with dedicated features in an ontology repository; however, the new AgroPortal's model is not a new vocabulary as it relies on preexisting ones. A generalization of this work is studied in a community-driven standardization effort in the context of the RDA Vocabulary and Semantic Services Interest Group
Building a biomedical ontology recommender web service
<p>Abstract</p> <p>Background</p> <p>Researchers in biomedical informatics use ontologies and terminologies to annotate their data in order to facilitate data integration and translational discoveries. As the use of ontologies for annotation of biomedical datasets has risen, a common challenge is to identify ontologies that are best suited to annotating specific datasets. The number and variety of biomedical ontologies is large, and it is cumbersome for a researcher to figure out which ontology to use.</p> <p>Methods</p> <p>We present the <it>Biomedical Ontology Recommender web service</it>. The system uses textual metadata or a set of keywords describing a domain of interest and suggests appropriate ontologies for annotating or representing the data. The service makes a decision based on three criteria. The first one is <it>coverage</it>, or the ontologies that provide most terms covering the input text. The second is <it>connectivity</it>, or the ontologies that are most often mapped to by other ontologies. The final criterion is <it>size</it>, or the number of concepts in the ontologies. The service scores the ontologies as a function of scores of the annotations created using the National Center for Biomedical Ontology (NCBO) <it>Annotator web service</it>. We used all the ontologies from the UMLS Metathesaurus and the NCBO BioPortal.</p> <p>Results</p> <p>We compare and contrast our Recommender by an exhaustive functional comparison to previously published efforts. We evaluate and discuss the results of several recommendation heuristics in the context of three real world use cases. The best recommendations heuristics, rated ‘very relevant’ by expert evaluators, are the ones based on coverage and connectivity criteria. The Recommender service (alpha version) is available to the community and is embedded into BioPortal.</p
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