Tuning Fuzzy SPARQL Queries in a Fuzzy Logic Programming Environment

We have recently designed FSA-SPARQL, an extension of the SPARQL query language for querying fuzzy RDF datasets. Answers of FSA-SPARQL queries are usually annotated with truth degrees which are computed from fuzzy connectives and operators that act on truth degrees associated to RDF triples. While FSA-SPARQL offers a rich repertoire of fuzzy connectives and operators, it is not always easy to retrieve the user's expected answers. This is very often due to wrong formulation of queries, caused by inadequate use/combination of fuzzy connectives, operators and thresholds. For instance, a high threshold for truth degrees in some RDF datasets can lead to an empty set of answers, some strong or weak restrictive combination of fuzzy conditions might produce few or too many answers, etc. On the other hand, our research group has also developed the fuzzy logic programming language $\mathcal{F}\mathcal{A}\mathcal{S}\mathcal{I}\mathcal{L}\mathcal{L}$, which has been equipped with tuning techniques for enabling the customization of queries from test cases. In this paper, our goals are: (1) to provide a FSA-SPARQL translation to $\mathcal{F}\mathcal{A}\mathcal{S}\mathcal{I}\mathcal{L}\mathcal{L}$ and (2) apply the tuning techniques to FSA-SPARQL queries for getting more precise formulation of queries from test cases.