2018 IEEE 34th International Conference on Data Engineering (ICDE) (2018)
Apr 16, 2018 to Apr 19, 2018
Inference of queries from their output examples has been extensively studied in multiple contexts as means to ease the formulation of queries. In this paper we propose a novel approach for the problem, based on provenance. The idea is to use provenance in two manners: first as an additional information that is associated with the given examples and explains their rationale; and then again as a way to show users a description of the difference between candidate queries, prompting their feedback. We have implemented the framework in the context of simple graph patterns and union thereof, and demonstrate its effectiveness in the context of multiple ontologies.
graph theory, inference mechanisms, ontologies (artificial intelligence), query languages, query processing
E. Abramovitz, D. Deutch and A. Gilad, "Interactive Inference of SPARQL Queries Using Provenance," 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France, 2018, pp. 581-592.