An Expressive and Efficient Solution to the Service Selection Problem

Abstract

Abstract. Given the large number of Semantic Web Services that can be created from online sources by using existing annotation tools, expressive formalisms and efficient and scalable approaches to solve the service selection problem are required to make these services widely available to the users. In this paper, we propose a framework that is grounded on logic and the Local-As-View approach for representing instances of the service selection problem. In our approach, Web services are semantically described using LAV mappings in terms of generic concepts from an ontology, user requests correspond to conjunctive queries on the generic concepts and, in addition, the user may specify a set of preferences that are used to rank the possible solutions to the given request. The LAV formulation allows us to cast the service selection problem as a query rewriting problem that must consider the relationships among the concepts in the ontology and the ranks induced by the preferences. Then, building on related work, we devise an encoding of the resulting query rewriting problem as a logical theory whose models are in correspondence with the solutions of the user request, and in presence of preferences, whose best models are in correspondence with the best-ranked solutions. Thus, by exploiting known properties of modern SAT solvers, we provide an efficient and scalable solution to the service selection problem. The approach provides the basis to represent a large number of real-world situations and interesting user requests.

ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints

“…Following the approach developed in previous work [11], the Local As View (LAV) approach is used to describe endpoints in terms of the ontology used in the endpoint dataset. Further, mediators implement query rewriting techniques, decompose queries into sub-queries against the endpoints, and gather data retrieved from the contacted endpoints.…”

“…Figure 3 illustrates the process performed when a main memory failure occurs and the timestamps of the stored tuples. Figure 2, annotated with the probe and insert times of the tuples, and the RJTs probe times 11 . Thus, we can say that B1 was probed at time 1 and inserted in L A at 2; also, timestamp 7 associated with the RJT of r1 in L A , indicates that the last probe of a tuple from source B was performed against this RJT at time 7.…”

SemLAV: Local-As-View Mediation for SPARQL Queries

“…Several approaches have been defined, e.g., MCD-SAT [14], GQR [4], Bucket Algorithm [25], and MiniCon [11]. Recently, Le et al, [17] propose a solution to identify and combine GAV SPARQL views that rewrite SPARQL queries against a global vocabulary, and Izquierdo et al [16] extend the MCDSAT rewriter with preferences to identify the combination of semantic services that rewrite a user request. Recently, Montoya et al propose GUN [26], a strategy to maximize the number of answers obtained from a given set of k rewritings; GUN aggregates the data obtained from the relevant views present in those k rewritings and executes the query over it.…”

SemLAV: Local-As-View Mediation for SPARQL Queries

“…In order to decide which rewriting engine will be use to compare with SemLAV, we run some preliminary experiments to compare existing state-of-the-art rewriting engines. We consider GQR [4], MCDSAT [14], MiniCon [15], and SSDSAT [16]. We execute these engines for 10 minutes and measure execution time and the number of rewritings generated by each engine.…”