Provenance in Web Applications JANUARY/FEBRUARY 2011 1089-7801/11/$26.00 © 2011 IEEE Published by the IEEE Computer Society 31 T he recent W3C Linking Open Data initiative boosts the publica- tion and interlinkage of massive amounts of datasets on the Semantic Web as Resource Description Frame- work (RDF) data queried with the SPARQL query language (see www. linkeddata.org and www.w3.org/tr/rdf- sparql-query). Together with other Web 2.0 technologies (such as mashups), this initiative has essentially transformed the Web from a publishing-only envi- ronment into a vibrant place for infor- mation dissemination in which data is exchanged, integrated, and material- ized in distributed repositories behind SPARQL endpoints. In this open environment, where Semantic Web data is represented by incomplete or replicated sets of RDF triples, it’s crucial to be able to assert the trustworthiness, reputation, and reliability of published information. This functionality essentially calls for representing and reasoning with the provenance of Semantic Web data manipulated by SPARQL queries. For instance, in the case of trust assess- ment 1 (one of the key applications recognized by the W3C Provenance Incubator Group), query result trust- worthiness is determined based on the trustworthiness of the data sources from which they’re derived. For sim- ple Boolean trust assessment, we need to determine only which output data should be trusted. For ranked trust assessment, we need to choose the Capturing trustworthiness, reputation, and reliability of Semantic Web data manipulated by SPARQL requires researchers to represent adequate provenance information, usually modeled as source data annotations and propagated to query results along with query evaluation. Alternatively, abstract provenance models can capture the relationship between query results and source data by taking into account the employed query operators. The authors argue the beneits of the latter for settings in which query results are materialized in several repositories and analyzed by multiple users. They also investigate how relational provenance models can be leveraged for SPARQL queries, and advocate for new provenance models. Yannis Theoharis, Irini Fundulaki, Grigoris Karvounarakis, and Vassilis Christophides Forth-ICS On Provenance of Queries on Semantic Web Data