Querying DL-lite Knowledge Bases from Hidden Datasets *

Ghassen Hamdi , Mohamed Nazih Omri , Odile Papini (1, 2) , Salem Benferhat (3) , Zied Bouraoui (3)

1 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
2 LIRICA - Logique, Interaction, Raisonnement et Inférence, Complexité, Algèbre
3 CRIL - Centre de Recherche en Informatique de Lens

One of the main aims of Ontology-Based Data Access is to uniform querying data using knowledge encoded in an ontol-ogy. Data are often provided by several information sources, and this has led to a number of methods that merge them in order to get a unified point of view. Existing merging approaches relie on the fact that the content of the datasets is known. However in many applications such as in recommendation sites, this prior knowledge about the content of the datasets is not available. This paper investigates the problem of querying multiple information sources without knowing the content of the datasets. We first provide several strategies to answer queries from datasets. We then study how these strategies can be compared to each other from a productivity point of view.

Domaines: Informatique [cs]

Ghassen Hamdi, Mohamed Nazih Omri, Odile Papini, Salem Benferhat, Zied Bouraoui. Querying DL-lite Knowledge Bases from Hidden Datasets *. International Symposium on Artificial Intelligence and Mathematics, Jan 2018, Fort Lauderdale, United States. ⟨hal-02084470⟩