http://precedings.nature.com/tags/RDF Recently posted documents tagged with 'RDF' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3775.1 Key assertions are extracted from “conclusions” sections of PubMed abstracts andconverted into Semantic Web / Linked Data format. The results are made accessible viafiles, a SPARQL endpoint, and a faceted search interface. Conclusion sections areidentified as valuable resources for machine-augmented key assertion identification andintegration in the biomedical domain. Results are discussed and opportunities for futurework and cooperation are highlighted. http://dx.doi.org/10.1038/npre.2009.3775.1 Mon, 21 Sep 2009 08:29:29 UTC Extracting conclusion sections from PubMed abstracts for rapid key assertion integration in biomedical research doi:10.1038/npre.2009.3775.1 2009-09-21 Nature Precedings 2009-09-21T08:29:29Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3562/version/1 While OWL, the Web Ontology Language, is often regarded as the preferred language for Knowledge Representation in the world of the Semantic Web, the potential of direct representation in RDF, the Resource Description Framework, is underestimated. Here we show how ontologies adequately represented in RDF could be semantically enriched with SPARUL. To deal with the semantics of relations we created Metarel, a meta-ontology for relations. The utility of the approach is demonstrated by an application on Gene Ontology Annotation (GOA) RDF graphs in the RDF Knowledge Base BioGateway. We show that Metarel can facilitate inferencing in BioGateway, which allows for queries that are otherwise not possible. Metarel is available on http://www.metarel.org. http://precedings.nature.com/documents/3562/version/1 Thu, 06 Aug 2009 19:10:14 UTC Metarel: an Ontology to support the inferencing of Semantic Web relations within Biomedical Ontologies hdl:10101/npre.2009.3562.1 2009-08-06 Ward Blondé Nature Precedings 2009-08-06T19:10:14Z Manuscript Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3463.1 Metarel allows the creation of millions of relational inferences in the RDF Knowledge Base BioGateway, by using five types of SPARUL closure queries. Metarel can be found at http://www.metarel.org. http://dx.doi.org/10.1038/npre.2009.3463.1 Mon, 27 Jul 2009 20:20:31 UTC Metarel: An Ontology to Support the Inferencing of Semantic Web Relations within Biomedical Ontologies doi:10.1038/npre.2009.3463.1 2009-07-27 Ward Blondé Nature Precedings 2009-07-27T20:20:31Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3193.1 The UniProt knowledgebase (UniProtKB) is a comprehensive repository of protein sequence and annotation data. We collect information from the scientific literature and other databases and provide links to over one hundred biological resources. Such links between different databases are an important basis for data integration, but the lack of a common standard to represent and link information makes data integration an expensive business. At UniProt we have started to tackle this problem by using the Resource Description Framework (http://www.w3.org/RDF/) to represent our data. RDF is a core technology for the World Wide Web Consortium’s Semantic Web activities (http://www.w3.org/2001/sw/) and is therefore well suited to work in a distributed and decentralized environment. The RDF data model represents arbitrary information as a set of simple statements of the form subject-predicate-object. To enable the linking of data on the Web, RDF requires that each resource must have a (globally) unique identifier. These identifiers allow everybody to make statements about a given resource and, together with the simple structure of the RDF data model, make it easy to combine the statements made by different people (or databases) to allow queries across different datasets. RDF is thus an industry standard that can make a major contribution to solve two important problems of bioinformatics: distributed annotation and data integration. http://dx.doi.org/10.1038/npre.2009.3193.1 Tue, 28 Apr 2009 18:42:07 UTC UniProt in RDF: Tackling Data Integration and Distributed Annotation with the Semantic Web doi:10.1038/npre.2009.3193.1 2009-04-28 Nicole Redaschi Nature Precedings 2009-04-28T18:42:07Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/945/version/2 Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value. With the already substantial and constantly expanding data generation capacity of the life sciences as well as the concomitant increase in the knowledge distributed in scientific articles, new ways to produce semantic annotations of this information are crucial. While automated techniques certainly facilitate the process, manual annotation remains the gold standard in most domains. In this manuscript, we describe a prototype mass-collaborative semantic annotation system that, by distributing the annotation workload across the broad community of biomedical researchers, may help to produce the volume of meaningful annotations needed by modern biomedical science. We present E.D., the Entity Describer, a mashup of the Connotea social tagging system, an index of semantic web-accessible controlled vocabularies, and a new public RDF database for storing social semantic annotations. http://precedings.nature.com/documents/945/version/2 Mon, 24 Sep 2007 11:49:10 UTC Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer hdl:10101/npre.2007.945.2 2007-09-24 Benjamin M. Good Nature Precedings 2007-09-24T11:49:10Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://precedings.nature.com/documents/945/version/1 Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value. With the already substantial and constantly expanding data generation capacity of the life sciences as well as the concomitant increase in the knowledge distributed in scientific articles, new ways to produce semantic annotations of this information are crucial. While automated techniques certainly facilitate the process, manual annotation remains the gold standard in most domains. In this manuscript, we describe a prototype mass-collaborative semantic annotation system that, by distributing the annotation workload across the broad community of biomedical researchers, may help to produce the volume of meaningful annotations needed by modern biomedical science. We present E.D., the Entity Describer, a mashup of the Connotea social tagging system, an index of semantic web-accessible controlled vocabularies, and a new public RDF database for storing social semantic annotations. http://precedings.nature.com/documents/945/version/1 Fri, 07 Sep 2007 16:54:21 UTC Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer hdl:10101/npre.2007.945.1 2007-09-07 Benjamin M. Good Nature Precedings 2007-09-07T16:54:21Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/2.5/