Abstract
Assessing the quality of linked data currently published on the Web is a crucial need of various data-intensive applications. Extensive work on similar applications for relational data and queries has shown that data provenance can be used in order to compute trustworthiness, reputation and reliability of query results, based on the source data and query operators involved in their derivation. In particular, abstract provenance models can be employed to record information about source data and query operators during query evaluation, and later be used e.g., to assess trust for individual query results. In this paper, we investigate the extent to which relational provenance models can be leveraged for capturing the provenance of SPARQL queries over linked data, and identify their limitations. To overcome these limitations, we advocate the need for new provenance models that capture the full expressive power of SPARQL, and can be used to support assessment of various forms of data quality for linked data manipulated declaratively by such queries.
An earlier version of this paper appeared in IEEE Internet Computing 15(1): 31-39, 2011.
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Karvounarakis, G., Fundulaki, I., Christophides, V. (2013). Provenance for Linked Data. In: Tannen, V., Wong, L., Libkin, L., Fan, W., Tan, WC., Fourman, M. (eds) In Search of Elegance in the Theory and Practice of Computation. Lecture Notes in Computer Science, vol 8000. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41660-6_19
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