Making Linked Open Data Writable with Provenance Semirings

Linked Open Data cloud (LOD) is essentially read-only, re- straining the possibility of collaborative knowledge construction. To sup- port collaboration, we need to make the LOD writable. In this paper, we propose a vision for a writable linked data where each LOD participant can define updatable materialized views from data hosted by other par- ticipants. Consequently, building a writable LOD can be reduced to the problem of SPARQL self-maintenance of Select-Union recursive mate- rialized views. We propose TM-Graph, an RDF-Graph annotated with elements of a specialized provenance semiring to maintain consistency of these views and we analyze complexity in space and traffic

Col-Graph: Towards Writable and Scalable Linked Open Data

International audienceLinked Open Data faces severe issues of scalability, availability and data quality. these issues are observed by data consumers performing federated queries; SPARQL endpoints do not respond and results can be wrong or out-of-date. If a data consumer finds an error, how can she fix it? This raises the issue of the writability of Linked Data. In this paper, we devise aan extension of the federation of Linked Data to data consumers. A data consumer can make partial copies of different datasets and make them available through a SPARQL endpoint. A data consumer can update her local copy and share updates with data providers and consumers. Update sharing improves general data quality, and replicated data creates opportunities for federated query engines to improve availability. However, when updates occur in an uncontrolled way, consistency issues arise. In this paper, we define fragments as SPARQL CONSTRUCT queries and propose a correction criterion to maintain these fragments incrementally without reevaluating the query. We define a coordination free protocol based on the counting of triples derivations and provenance. We analyze the theoretical complexity of the protocol in time, space and traffic. Experimental results suggest the scalability of our approach