02649nas a2200277 4500008004100000245009100041210006900132260003400201300001400235490000900249520174100258653003601999653001902035653003002054653003702084653002202121653002002143100002102163700002502184700002002209700001602229700003202245700001902277700002402296856005102320 2010 eng d00aProvenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data0 aProvenance Context Entity PaCE Scalable Provenance Tracking for aHeidelberg, GermanybSpringer a461–4700 v61873 a
The Semantic Web Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current Semantic Web provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples that hinders data sharing. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples without the use of RDF reification or blank nodes. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine to support provenance tracking on RDF data extracted from multiple sources, including biomedical literature and the UMLS Metathesaurus. The evaluations demonstrate a minimum of 49% reduction in total number of provenancespecific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, using the PACE approach improves the performance of complex provenance queries by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.
10aBiomedical knowledge repository10aContext theory10aProvenance context entity10aProvenance Management Framework.10aProvenir ontology10aRDF reification1 aSahoo, Satya, S.1 aBodenreider, Olivier1 aHitzler, Pascal1 aSheth, Amit1 aThirunarayan, Krishnaprasad1 aGertz, Michael1 aLudäscher, Bertram uhttp://dx.doi.org/10.1007/978-3-642-13818-8_3200588nas a2200169 4500008004100000245005900041210005900100260002300159100002300182700002100205700002100226700002300247700001700270700001600287700001900303856009600322 2009 eng d00aOntology Driven Integration of Biology Experiment Data0 aOntology Driven Integration of Biology Experiment Data aCleveland, OH, USA1 aMutharaju, Raghava1 aSahoo, Satya, S.1 aWeatherly, Brent1 aAnantharam, Pramod1 aLogan, Flora1 aSheth, Amit1 aTarleton, Rick uhttps://daselab.cs.ksu.edu/publications/ontology-driven-integration-biology-experiment-data02427nas a2200229 4500008004100000245009100041210006900132260003400201300001500235490000900250520170100259100002101960700002101981700002302002700002302025700001602048700001902064700002102083700002302104700001902127856005102146 2009 eng d00aOntology-Driven Provenance Management in eScience: An Application in Parasite Research0 aOntologyDriven Provenance Management in eScience An Application aVilamoura, PortugalbSpringer a992–10090 v58713 aProvenance, from the French word “provenir”, describes the lineage or history of a data entity. Provenance is critical information in scientific applications to verify experiment process, validate data quality and associate trust values with scientific results. Current industrial scale eScience projects require an end-to-end provenance management infrastructure. This infrastructure needs to be underpinned by formal semantics to enable analysis of large scale provenance information by software applications. Further, effective analysis of provenance information requires well-defined query mechanisms to support complex queries over large datasets. This paper introduces an ontology-driven provenance management infrastructure for biology experiment data, as part of the Semantic Problem Solving Environment (SPSE) for Trypanosoma cruzi (T.cruzi). This provenance infrastructure, called T.cruzi Provenance Management System (PMS), is underpinned by (a) a domain-specific provenance ontology called Parasite Experiment ontology, (b) specialized query operators for provenance analysis, and (c) a provenance query engine. The query engine uses a novel optimization technique based on materialized views called materialized provenance views (MPV) to scale with increasing data size and query complexity. This comprehensive ontology-driven provenance infrastructure not only allows effective tracking and management of ongoing experiments in the Tarleton Research Group at the Center for Tropical and Emerging Global Diseases (CTEGD), but also enables researchers to retrieve the complete provenance information of scientific results for publication in literature.
1 aSahoo, Satya, S.1 aWeatherly, Brent1 aMutharaju, Raghava1 aAnantharam, Pramod1 aSheth, Amit1 aTarleton, Rick1 aMeersman, Robert1 aDillon, Tharam, S.1 aHerrero, Pilar uhttp://dx.doi.org/10.1007/978-3-642-05151-7_1800638nas a2200169 4500008004100000245007800041210006900119260002300188100002300211700002100234700002100255700001700276700002300293700001600316700001900332856011700351 2009 eng d00aTrykipedia: Collaborative Bio-Ontology Development using Wiki Environment0 aTrykipedia Collaborative BioOntology Development using Wiki Envi aCleveland, OH, USA1 aAnantharam, Pramod1 aSahoo, Satya, S.1 aWeatherly, Brent1 aLogan, Flora1 aMutharaju, Raghava1 aSheth, Amit1 aTarleton, Rick uhttps://daselab.cs.ksu.edu/publications/trykipedia-collaborative-bio-ontology-development-using-wiki-environment