732 research outputs found

    Relations as patterns: bridging the gap between OBO and OWL.

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    BACKGROUND: Most biomedical ontologies are represented in the OBO Flatfile Format, which is an easy-to-use graph-based ontology language. The semantics of the OBO Flatfile Format 1.2 enforces a strict predetermined interpretation of relationship statements between classes. It does not allow flexible specifications that provide better approximations of the intuitive understanding of the considered relations. If relations cannot be accurately expressed then ontologies built upon them may contain false assertions and hence lead to false inferences. Ontologies in the OBO Foundry must formalize the semantics of relations according to the OBO Relationship Ontology (RO). Therefore, being able to accurately express the intended meaning of relations is of crucial importance. Since the Web Ontology Language (OWL) is an expressive language with a formal semantics, it is suitable to de ne the meaning of relations accurately. RESULTS: We developed a method to provide definition patterns for relations between classes using OWL and describe a novel implementation of the RO based on this method. We implemented our extension in software that converts ontologies in the OBO Flatfile Format to OWL, and also provide a prototype to extract relational patterns from OWL ontologies using automated reasoning. The conversion software is freely available at http://bioonto.de/obo2owl, and can be accessed via a web interface. CONCLUSIONS: Explicitly defining relations permits their use in reasoning software and leads to a more flexible and powerful way of representing biomedical ontologies. Using the extended langua0067e and semantics avoids several mistakes commonly made in formalizing biomedical ontologies, and can be used to automatically detect inconsistencies. The use of our method enables the use of graph-based ontologies in OWL, and makes complex OWL ontologies accessible in a graph-based form. Thereby, our method provides the means to gradually move the representation of biomedical ontologies into formal knowledge representation languages that incorporates an explicit semantics. Our method facilitates the use of OWL-based software in the back-end while ontology curators may continue to develop ontologies with an OBO-style front-end

    Modelos de representación de imprecisión e incertidumbre en fusión de alto nivel

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    Actas de: XVII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2014). Zaragoza, 5-7 de febrero de 2014.Las técnicas de fusión de datos e información procedente de redes de sensores necesitan manejar información incierta e imprecisa, puesto que es habitual enfrentarse a problemas en los que el conocimiento disponible es vago o insuficiente y/o los aparatos de medición están sujetos a fallos. Con el reciente auge de la denominada "fusión de alto nivel", que tiene como objetivo reconocer la situación observada e identificar posibles riesgos, este problema se ha acentuado, ya que los formalismos que se utilizan habitualmente para construir un modelo simbólico del escenario, como la lógica de primer orden y las ontologías, no proporcionan soporte para este tipo de conocimiento. En este trabajo repasamos varias propuestas recientes para representación y razonamiento con información incierta e imprecisa en fusión de alto nivel. Nos centramos en dos tipos: (a) las que incorporan estos mecanismos en los propios modelos de representación, como las ontologías probabilísticas y difusas y las redes lógicas de Markov; (b) las que extienden el proceso de fusión con una capa de gestión de incertidumbre adicional, como las basadas en argumentación probabilística.Este trabajo ha sido financiado por la Junta de Andalucía (P11-TIC-7460), la Comunidad de Madrid (S2009/TIC- 1485) y el Ministerio de Economía y Competitividad de España (TEC2012-37832-C02-01, TEC2011-28626-C02- 02, TIN2012-30939).Publicad

    Interoperability of semantics in news production

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    The landscape of multimedia ontologies in the last decade

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    Many efforts have been made in the area of multimedia to bridge the socalled “semantic-gap” with the implementation of ontologies from 2001 to the present. In this paper, we provide a comparative study of the most well-known ontologies related to multimedia aspects. This comparative study has been done based on a framework proposed in this paper and called FRAMECOMMON. This framework takes into account process-oriented dimension, such as the methodological one, and outcome-oriented dimensions, like multimedia aspects, understandability, and evaluation criteria. Finally, we derive some conclusions concerning this one decade state-of-art in multimedia ontologies

    MicrO: an ontology of phenotypic and metabolic characters, assays, and culture media found in prokaryotic taxonomic descriptions

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    Background: MicrO is an ontology of microbiological terms, including prokaryotic qualities and processes, material entities (such as cell components), chemical entities (such as microbiological culture media and medium ingredients), and assays. The ontology was built to support the ongoing development of a natural language processing algorithm, MicroPIE (or, Microbial Phenomics Information Extractor). During the MicroPIE design process, we realized there was a need for a prokaryotic ontology which would capture the evolutionary diversity of phenotypes and metabolic processes across the tree of life, capture the diversity of synonyms and information contained in the taxonomic literature, and relate microbiological entities and processes to terms in a large number of other ontologies, most particularly the Gene Ontology (GO), the Phenotypic Quality Ontology (PATO), and the Chemical Entities of Biological Interest (ChEBI). We thus constructed MicrO to be rich in logical axioms and synonyms gathered from the taxonomic literature. Results: MicrO currently has similar to 14550 classes (similar to 2550 of which are new, the remainder being microbiologically-relevant classes imported from other ontologies), connected by similar to 24,130 logical axioms (5,446 of which are new), and is available at (http://purl.obolibrary.org/obo/MicrO.owl) and on the project website at https://github.com/carrineblank/MicrO. MicrO has been integrated into the OBO Foundry Library (http://www.obofoundry.org/ontology/micro.html), so that other ontologies can borrow and re-use classes. Term requests and user feedback can be made using MicrO's Issue Tracker in GitHub. We designed MicrO such that it can support the ongoing and future development of algorithms that can leverage the controlled vocabulary and logical inference power provided by the ontology. Conclusions: By connecting microbial classes with large numbers of chemical entities, material entities, biological processes, molecular functions, and qualities using a dense array of logical axioms, we intend MicrO to be a powerful new tool to increase the computing power of bioinformatics tools such as the automated text mining of prokaryotic taxonomic descriptions using natural language processing. We also intend MicrO to support the development of new bioinformatics tools that aim to develop new connections between microbial phenotypes and genotypes (i.e., the gene content in genomes). Future ontology development will include incorporation of pathogenic phenotypes and prokaryotic habitats.This work was funded by grants from the National Science Foundation (award DEB-1208534 to CEB, DEB-1208567 to HC, and DEB-1208685 to LRM) and by a travel grant (to CEB) to attend the 2013 NESCent Ontologies for Evolutionary Biology workshop. RW was supported by CyVerse and the National Science Foundation under award numbers DBI-0735191 and DBI-1265383. Many thanks to Elvis Hsin-Hui Wu (University of Arizona), Gail Gasparich (Towson University), and Gordon Burleigh (University of Florida) for comments and/or assistance with ontology construction and compilation of taxonomic descriptions. We would also like to thank Chris Mungall (LBNL), Oliver He (University of Michigan) for technical assistance with OntoBee and OntoFox, and Gareth Owen (ChEBI project leader, head curator) and other curators at ChEBI for assistance in the incorporation of microbial-specific chemical terms and synonyms into ChEBI. Thanks also to the instructors (Melissa Haendel, Matt Yoder, Jim Balhoff) and students of the 2013 NESCent Ontologies for Evolutionary Biology workshop, and to Karen Cranston (NESCent) and the support staff at NESCent. Thanks also to the OBI-devel team for comments regarding the overall structure of assay terms, and associated object properties, in MicrO.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

    Evaluating FAIR Digital Object and Linked Data as distributed object systems

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    FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building a ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes. We conclude with recommendations for both Linked Data and FDO communities to further their adaptation and alignment.Comment: 40 pages, submitted to PeerJ C

    Creation and extension of ontologies for describing communications in the context of organizations

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    Thesis submitted to Faculdade de Ciências e Tecnologia of the Universidade Nova de Lisboa, in partial fulfillment of the requirements for the degree of Master in Computer ScienceThe use of ontologies is nowadays a sufficiently mature and solid field of work to be considered an efficient alternative in knowledge representation. With the crescent growth of the Semantic Web, it is expectable that this alternative tends to emerge even more in the near future. In the context of a collaboration established between FCT-UNL and the R&D department of a national software company, a new solution entitled ECC – Enterprise Communications Center was developed. This application provides a solution to manage the communications that enter, leave or are made within an organization, and includes intelligent classification of communications and conceptual search techniques in a communications repository. As specificity may be the key to obtain acceptable results with these processes, the use of ontologies becomes crucial to represent the existing knowledge about the specific domain of an organization. This work allowed us to guarantee a core set of ontologies that have the power of expressing the general context of the communications made in an organization, and of a methodology based upon a series of concrete steps that provides an effective capability of extending the ontologies to any business domain. By applying these steps, the minimization of the conceptualization and setup effort in new organizations and business domains is guaranteed. The adequacy of the core set of ontologies chosen and of the methodology specified is demonstrated in this thesis by its effective application to a real case-study, which allowed us to work with the different types of sources considered in the methodology and the activities that support its construction and evolution
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