Abstract:

[Objective] Resource Description Framework (RDF), a standard model for knowledge representation, has been widely used in various scientific data management applications to represent the scientific data as a knowledge graph. Meanwhile, Simple Protocol And RDF Query Language (SPARQL) is a structured query language to access RDF repository. As more and more data publishers release their datasets in the model of RDF, how to integrate the RDF datasets provided by different data publishers into a federated RDF system becomes a challenge. [Coverage] In this paper we provide an overview of the studies of federated RDF systems. [Methods]The major differences among different federated RDF systems are different strategies for source selection guided query decomposition and query processing optimization. [Results] Existing query decomposition and source selection strategies in federated RDF systems can be divided into two categories: metadata-based and ASK-based strategies; Query optimization strategies in existing federated RDF systems are some joint optimizations based on System-R style dynamic programming. [Limitations] Existing federated RDF systems still do not discuss how to support SPARQL 1.1. [Conclusions] Federated RDF systems can integrate distributed RDF graphs among different sources, which means that it is an important future research direction.

Key words: Federated RDF Systems, SPARQL query evaluation, query optimization