Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning introduced in the 70's, which amounts to computing a least general generalization (lgg) of such descriptions. It has also started receiving consideration in Knowlegge Representation from the 90's, and recently in the Semantic Web field. We revisit this problem in the popular Resource Description Framework (RDF) of W3C, where descriptions are RDF graphs, i.e., a mix of data and knowledge. Notably, and in contrast to the literature, our solution to this problem holds for the entire RDF standard, i.e., we do not restrict RDF graphs in any way (neither their structure nor their semantics based on RDF entailment, i.e., inference) and, further, our algorithms can compute lggs of small-to-huge RDF graphs.
Abstract. Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning, which amounts to computing a least general generalization (lgg) of such descriptions. We revisit this old problem in the popular conjunctive fragment of SPARQL, a.k.a. Basic Graph Pattern Queries (BGPQs). In particular, we define this problem in all its generality by considering general BGPQs, while the literature considers unary tree-shaped BGPQs only. Further, when ontological knowledge is available as RDF Schema constraints, we take advantage of it to devise much more pregnant lggs.
Abstract. Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning. It was formalized in the early 70's as computing a least general generalization (lgg) of such descriptions. We revisit this well-established problem in the SPARQL query language for RDF graphs. In particular, and by contrast to the literature, we address it for the entire class of conjunctive SPARQL queries, a.k.a. Basic Graph Pattern Queries (BGPQs), and crucially, when background knowledge is available as RDF Schema ontological constraints, we take advantage of it to devise much more precise lggs, as our experiments on the popular DBpedia dataset show.
Federated SPARQL queries allow to query multiple interlinked datasets hosted by remote SPARQL endpoints. However, finding federated queries over a growing number of datasets is challenging. In this paper, we propose PFed, an approach to recommend plausible federated queries based on real query logs of different datasets. The problem is not to find similar federated queries, but plausible complementary queries over different datasets. Starting with a real SPARQL query from a given log, PFed stretches the query with real queries from different logs. To prune the research space, PFed proposes semantic summary to prune the query logs. Experimental results with real logs of DBpedia and SWDF demonstrate that PFed is able to prune drastically the logs and recommend plausible federated queries.
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