Optimizing FOL reducible query answering: understanding performance challenges

Damian Bursztyn (1, 2) , François Goasdoué (3, 4) , Ioana Manolescu (2, 1, 5)

1 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
2 CEDAR - Rich Data Analytics at Cloud Scale
3 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
4 SHAMAN - Symbolic and Human-centric view of dAta MANagement
5 Université Paris-Saclay

Semantic Web data management raises the challenge of answering queries under constraints (i.e., in the presence of implicit data). To bridge the gap between this extended setting and that of query evaluation provided by database engines, a reasoning step (w.r.t. the constraints) is necessary before query evaluation. A large and useful set of ontology languages enjoys FOL reducibility of query answering: queries can be answered by evaluating a SQLized first-order logic (FOL) formula (obtained from the query and the ontology) directly against the explicitly stored data (i.e., without considering the ontological constraints). Our demonstration showcases to the attendees, and analyzes, the performance of several reformulation-based query answering techniques, including one we recently described in [5], applied to the lightweight description logic DL-LiteR underpinning the W3C's OWL2 QL profile.

Domaines: Base de données [cs.DB]

Damian Bursztyn, François Goasdoué, Ioana Manolescu. Optimizing FOL reducible query answering: understanding performance challenges. ISWC 2016: The 15th International Semantic Web Conference, Oct 2016, Kobe, Japan. ⟨hal-01400568⟩