On the Feasibility of Handling Uncertainty in SPARQL Queries in the Case of Sparse Graphs (International Journal of Innovative Research in Computer Science & Technology, 2020)
The purpose of this paper is to explore the  possibility of evaluating SPARQL queries containing  probabilities using linear algebraic methods and techniques.  This approach has many important advantages, such as  simplicity, succinctness, elegance and greater familiarity to  a wide base of practitioners. On the other hand, there are  questions regarding its efficiency in view of the huge  datasets of the current era. In this paper, we advocate that in  the case of sparse RDF graphs we can resort to sparse  matrices for computing complicated probabilistic queries. It  is demonstrated that via probabilistic sparse matrices one  can evaluate specific types of queries involving transitive  predicates, which are of great practical importance. The  algorithm and data structures that are currently available for  handling sparse matrices promise improved performance in  pragmatic situations, which constitutes this line of approach  particularly promising.
Keywords: RDF graph; SPARQL query; probabilistic SPARQL query; sparse matrices; probabilistic matrices