Incremental characterization of RDF Triple Stores

Many semantic web applications integrate data from distributed triple stores and to be efficient, they need to know what kind of content each triple store holds in order to assess if it can contribute to its queries. We present an algorithm to build indexes summarizing the content of triple stores. We extended Depth-First Search coding to provide a canonical representation of RDF graphs and we introduce a new join operator between two graph codes to optimize the generation of an index. We provide an incremental update algorithm and conclude with tests on real datasets

DFS-based frequent graph pattern extraction to characterize the content of RDF Triple Stores

International audienceSemantic web applications often access distributed triple stores relying on different ontologies and maintaining bases of RDF annotations about different domains. Use cases often involve queries which results combine pieces of annotations distributed over several bases maintained on different servers. In this context, one key issue is to characterize the content of RDF bases to be able to identify their potential contributions to the processing of a query. In this paper we propose an algorithm to extract a compact representation of the content of an RDF repository. We first improve the canonical representation of RDF graphs based on DFS code proposed in the literature. We then provide a join operator to significantly reduce the number of frequent graph patterns generated from the analysis of the content of the base, and we reduce the index size by keeping only the graph patterns with maximal coverage. Our algorithm has been tested on different data sets as discussed in conclusion

Agents Handling Annotation Distribution in a Corporate Semantic Web

This article describes a multi-agent software architecture to manage a corporate memory in the form of a corporate semantic Web. It summarizes the design rationale and the final architecture of the CoMMA system. It then presents and discusses an approach to manage distributed annotations, focusing on two problems: the allocation of newly posted annotations and the allocation of the tasks involved in distributed query-solving