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Matches in ScholarlyData for { ?s ?p ?o. }

• item-8 hasContent michael-martin.
• item-8 next item-9.
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• item-9 type ListItem.
• item-9 hasContent pablo-n-mendes.
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• paper-35 type InProceedings.
• paper-35 label "Using SPARQL to Query BioPortal Ontologies and Metadata".
• paper-35 sameAs paper-35.
• paper-35 abstract "BioPortal is a repository of biomedical ontologies - the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other languages, as well as a large number of medical terminologies that the US National Library of Medicine distributes in its own proprietary format. We have published the RDF based serializations of all these ontologies and their metadata at sparql.bioontology.org. This dataset contains 203M triples, representing both content and metadata for the 300+ ontologies; and 9M mappings between terms. This endpoint can be queried with SPARQL which opens new usage scenarios for the biomedical domain. This paper presents lessons learned from having redesigned several applications that today use this SPARQL endpoint to consume ontological data.".
• paper-35 hasAuthorList authorList.
• paper-35 isPartOf proceedings-1.
• paper-35 title "Using SPARQL to Query BioPortal Ontologies and Metadata".
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• authorList hasItem item-6.
• authorList hasLastItem item-6.
• item-1 type ListItem.
• item-1 hasContent manuel-salvadores.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent matthew-horridge.
• item-2 next item-3.
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• item-3 type ListItem.
• item-3 hasContent paul-alexander.
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• item-4 type ListItem.
• item-4 hasContent ray-w-fergerson.
• item-4 next item-5.
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• item-5 type ListItem.
• item-5 hasContent mark-a-musen.
• item-5 next item-6.
• item-5 previous item-4.
• item-6 type ListItem.
• item-6 hasContent natasha-f-noy.
• item-6 previous item-5.
• paper-36 type InProceedings.
• paper-36 label "ourSpaces - Design and Deployment of a Semantic Virtual Research Environment".
• paper-36 sameAs paper-36.
• paper-36 abstract "In this paper we discuss our experience with the design, development and deployment of the ourSpaces Virtual Research Environment. ourSpaces makes use of Semantic Web technologies to create a platform to support multidisciplinary research groups. This paper introduces the main semantic components of the system: a framework to capture the provenance of the research process, a collection of services to create and visualise metadata and a policy reasoning service. We also describe different approaches to support interaction between users and metadata within the VRE. We discuss the lessons learnt during the deployment process with three case study groups. Finally, we present our conclusions and future directions for exploration in terms of developing ourSpaces further.".
• paper-36 hasAuthorList authorList.
• paper-36 isPartOf proceedings-1.
• paper-36 title "ourSpaces - Design and Deployment of a Semantic Virtual Research Environment".
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• authorList hasFirstItem item-1.
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• authorList hasLastItem item-6.
• item-1 type ListItem.
• item-1 hasContent peter-edwards.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent edoardo-pignotti.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent alan-eckhardt.
• item-3 next item-4.
• item-3 previous item-2.
• item-4 type ListItem.
• item-4 hasContent kapila-ponnamperuma.
• item-4 next item-5.
• item-4 previous item-3.
• item-5 type ListItem.
• item-5 hasContent chris-mellish.
• item-5 next item-6.
• item-5 previous item-4.
• item-6 type ListItem.
• item-6 hasContent thomas-bouttaz.
• item-6 previous item-5.
• paper-37 type InProceedings.
• paper-37 label "Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions".
• paper-37 sameAs paper-37.
• paper-37 abstract "Diagnosis, or the method to connect causes to its effects, is an important reasoning task for obtaining insight on cities and reaching the concept of sustainable and smarter cities that is envisioned nowadays. This paper, focusing on transportation and its road traffic, presents how road traffic congestions can be detected and diagnosed in quasi real-time. We adapt pure Artificial Intelligence diagnosis techniques to fully exploit knowledge which is captured through relevant semantics-augmented stream and static data from various domains. Our prototype of semantic-aware diagnosis of road traffic congestions, experimented in Dublin Ireland, works efficiently with large, heterogeneous information sources and delivers value-added services to citizens and city managers in quasi real-time.".
• paper-37 hasAuthorList authorList.
• paper-37 isPartOf proceedings-1.
• paper-37 title "Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions".
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• authorList hasItem item-1.
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• authorList hasItem item-3.
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• item-1 type ListItem.
• item-1 hasContent freddy-lecue.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent anika-schumann.

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