Ontology-mediated query answering (OMQA) consists in asking database queries on knowledge bases (KBs); a KB is a set of facts called the KB's database, which is described by domain knowledge called the KB's ontology. A widely-investigated OMQA technique is FO-rewriting: every query asked on a KB is reformulated w.r.t. the KB's ontology, so that its answers are computed by the relational evaluation of the query reformulation on the KB's database. Crucially, because FO-rewriting compiles the domain knowledge relevant to queries into their reformulations, query reformulations may be complex and their optimization is the crux of efficiency.

This paper deals with the problem of efficiently computing the most preferred objects, in the sense of Pareto ordering, in a set of multidimensional objects involving outliers. These outliers, for instance, odd data from ad sales websites, can hide interesting points, so it is important to limit their impact. To do this, we propose a graded Skyline based on the typicality of each object. Such an approach allows to supply an α-cut of the skyline containing both nondominated points, and almost nondominated but more representative points. The computational cost of a naive algorithm performing this task is very high, especially in high-dimensional spaces, due to the large number of comparisons between objects. Since common skyline optimizations cannot be used in our approach, we propose an implementation on parallel architectures, CUDA and OpenMP. Experimental results on both real-world and synthetic data sets show that this processing strategy makes the approach scalable.

This paper presents an approach aimed at mining a new type of pattern in data, namely analogical proportions. An analogical proportion expresses the equality of the relationships between the attributes of two pairs of structured objects. This notion is investigated in the database context for the discovery of different forms of "parallels" between pairs of tuples. First, we give a formal definition of the analogical proportion in the setting of relational databases. Then we focus on the problem of mining analogical proportions. We propose to use a clustering approach for enumerating parallels occurring in a relation, thus discovering analogical proportions.

AGGREGO SEARCH offers a novel keyword-based query solu-tion for end users in order to retrieve precise answers from se-mantic data sources. Contrary to existing approaches, AGGREGO SEARCH suggests grammatical connectors from natural languages during the query formulation step in order to specify the meaning of each keyword, thus leading to a complete and explicit definition of the intent of the search. An example of such a query is name of person at the head of company and author of article about “busi-ness intelligence". In order to help users formulate such connected keywords queries, a specific autocompletion strategy has been de-veloped. A translation of the user keyword query into SPARQL is performed on-the-fly during the interactive query construction pro-cess. For this demonstration, we show how AGGREGO SEARCH has been integrated on top of a mediation system to let users intu-itively define explicit and precise keyword queries in order to ex-tract knowledge distributed in heterogeneous large semantic data sources. 1.

This paper proposes a notion of fuzzy graph database and describes a fuzzy query algebra that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. The algebra, based on fuzzy set theory and the concept of a fuzzy graph, is composed of a set of operators that can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. In a similar way as relational algebra constitutes the basis of SQL, the fuzzy algebra proposed here underlies a user-oriented query language and an associated tool implementing this language that are also presented in the paper.