Learning Commonalities in RDF

Abstract

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.

Learning Commonalities in SPARQL

“…We implemented our technical contributions in Java 1.8, on top of the Jena 3.0.1 RDF reasoner and of a PostgreSQL 9.3.11 server, all used with default settings; our implemented algorithms are detailed in [8].…”

“…We conducted experiments using real DBpedia data [17] and synthetic LUBM data [12]. For space reasons, we present only our DBpedia experiments; LUBM ones can be found in [8] and allow drawing similar conclusions.…”

“…Since then, it has also received consideration in the Knowledge Representation field, where least general generalizations were rebaptized least common subsumers [5], in Description Logics [1,5,14,27] and in Conceptual Graphs [3]. More recently, this problem started being investigated in RDF [7,9] and its associated SPARQL query language [2,10,16], the two prominent Semantic Web standards by W3C. Motivations.…”

“…Organization. Following the presentation of [8][9][10], we first recall the basics of RDF and SPARQL in Section 2. Then, we detail the above contributions.…”

“…Supplementary material (proofs of our technical results, implemented algorithms, additional experiments, etc) is available in our online research report [8].…”