@conference {112,
	title = {Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data},
	booktitle = {Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010},
	volume = {6187},
	year = {2010},
	pages = {461{\textendash}470},
	publisher = {Springer},
	organization = {Springer},
	address = {Heidelberg, Germany},
	abstract = {<p class="rtejustify">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.\&nbsp;</p>
},
	keywords = {Biomedical knowledge repository, Context theory, Provenance context entity, Provenance Management Framework., Provenir ontology, RDF reification},
	doi = {10.1007/978-3-642-13818-8_32},
	url = {http://dx.doi.org/10.1007/978-3-642-13818-8_32},
	author = {Satya S. Sahoo and Olivier Bodenreider and Pascal Hitzler and Amit Sheth and Krishnaprasad Thirunarayan},
	editor = {Michael Gertz and Bertram Lud{\"a}scher}
}
@conference {168,
	title = {Ontology Driven Integration of Biology Experiment Data},
	booktitle = {Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters \& Demos},
	year = {2009},
	address = {Cleveland, OH, USA},
	author = {Raghava Mutharaju and Satya S. Sahoo and D. Brent Weatherly and Pramod Anantharam and Flora Logan and Amit Sheth and Rick Tarleton}
}
@conference {162,
	title = {Ontology-Driven Provenance Management in eScience: An Application in Parasite Research},
	booktitle = {On the Move to Meaningful Internet Systems: OTM 2009, Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009, Proceedings, Part II},
	volume = {5871},
	year = {2009},
	pages = {992{\textendash}1009},
	publisher = {Springer},
	organization = {Springer},
	address = {Vilamoura, Portugal},
	abstract = {<p class="rtejustify">Provenance, from the French word {\textquotedblleft}provenir{\textquotedblright}, 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.</p>
},
	doi = {10.1007/978-3-642-05151-7_18},
	url = {http://dx.doi.org/10.1007/978-3-642-05151-7_18},
	author = {Satya S. Sahoo and D. Brent Weatherly and Raghava Mutharaju and Pramod Anantharam and Amit Sheth and Rick Tarleton},
	editor = {Robert Meersman and Tharam S. Dillon and Pilar Herrero}
}
@conference {169,
	title = {Trykipedia: Collaborative Bio-Ontology Development using Wiki Environment},
	booktitle = {Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters \& Demos},
	year = {2009},
	address = {Cleveland, OH, USA},
	author = {Pramod Anantharam and Satya S. Sahoo and D. Brent Weatherly and Flora Logan and Raghava Mutharaju and Amit Sheth and Rick Tarleton}
}
