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Reformulation-based query answering in RDF: alternatives and performance

Damian Bursztyn 1, 2 François Goasdoué 1, 3 Ioana Manolescu 1, 2
1 OAK - Database optimizations and architectures for complex large data
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
3 PILGRIM - Gradedness, Imprecision, and Mediation in Database Management Systems
Abstract : Answering queries over Semantic Web data, i.e., RDF graphs, must account for both explicit data and implicit data, entailed by the explicit data and the semantic constraints holding on them. Two main query answering techniques have been devised, namely Saturation-based (Sat) which precom-putes and adds to the graph all implicit information, and Reformulation-based (Ref) which reformulates the query based on the graph constraints, so that evaluating the refor-mulated query directly against the explicit data (i.e., without considering the constraints) produces the query answer. While Sat is well known, Ref has received less attention so far. In particular, reformulated queries often perform poorly if the query is complex. Our demonstration show-cases a large set of Ref techniques, including but not limited to one we proposed recently. The audience will be able to 1. test them against different datasets, constraints and queries, as well as different well-established systems, 2. analyze and understand the performance challenges they raise, and 3. alter the scenarios to visualize the impact on performance. In particular, we show how a cost-based Ref approach allows avoiding reformulation performance pitfalls.
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Submitted on : Wednesday, July 22, 2015 - 3:38:48 PM
Last modification on : Tuesday, October 19, 2021 - 11:58:53 PM
Long-term archiving on: : Friday, October 23, 2015 - 10:11:37 AM


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  • HAL Id : hal-01174298, version 1


Damian Bursztyn, François Goasdoué, Ioana Manolescu. Reformulation-based query answering in RDF: alternatives and performance. Very Large Data Bases, Aug 2015, Hawaii, United States. ⟨hal-01174298⟩



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