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Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data

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Title Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data
Author , , , ,
Venue SSDBM2010
Book The 22nd International Conference on Scientific and Statistical Database Management (SSDBM) 2010
Year 2010
Resource Type ConferencePaper
Keyword(s) semantic provenance,Provenance Management Framework,Provenir ontology,Provenance context entity,Biomedical knowledge repository,Context theory,RDF reification,domain specific provenance,Model theoretic semantics,PrOM,PACE,Provenance context
Full Citation Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth, and Krishnaprasad Thirunarayan, 'Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data,' In Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM'10), Michael Gertz and Bertram Ludascher (Eds.). Springer-Verlag, Berlin, Heidelberg, June 30-July 2, 2010, 461-470.
Abstract The 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 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. 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. 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. The evaluations demonstrate a minimum of 49% reduction in total number of provenance-specific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, performance for complex queries improves by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.
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