International audienceChoosing the tools for the management of large and semi-structured knowledge bases has always been considered as a quite crafty task. This is due to the emergence of different solutions in a short period of time, and also to the lack of benchmarking available solutions. In this paper, we use ALASKA, a logical framework, that enables the comparison of different storage solutions at the same logical level. ALASKA translates different data representation languages such as relational databases, graph structures or RDF triples into logics. We use the platform to load semi-structured knowledge bases, store, and perform conjunctive queries over relational and non-relational storage systems

The paper presents ALASKA, a multi-layered platform enabling to perform ontological conjunctive query answering (OCQA) over heterogeneously-stored knowledge bases in a generic, logic-based manner. While this problem knows today a renewed interest in knowledge-based systems with the semantic equivalence of different languages widely studied, from a practical view point this equivalence has been not made explicit. Moreover, the emergence of graph database provides competitive storage methods not yet addressed by existing literature.

In the context of ontological conjunctive query answering different paradigms for representation and their subsequent manipulation by dedicated reasoning systems have been successfully studied in the past. However, new challenges, problems and issues have appeared in the context of knowledge representation in AI that involve the logical manipulation of increasingly large information sets (see for example the Semantic Web). In this paper we explain these challenges by the means of an example and try to further identify the difficulties ahead of our goal.