In: Computing and Informatics, vol. 38, no. 3
J. Potoniec
Details:
Year, pages: 2020, 679 - 700
Language: eng
Keywords:
SPARQL, RDF, active learning
About article:
We present LSQ, an algorithm learning SPARQL queries from a subset of expected results. The algorithm leverages grouping, aggregates and inline values of SPARQL 1.1 in order to move most of the complex computations to a SPARQL endpoint. It operates by building and testing hypotheses expressed as SPARQL queries and uses active learning to collect a small number of learning examples from the user. We provide an open-source implementation and an on-line interface to test the algorithm. In the experimental evaluation, we use real queries posed in the past to the official DBpedia SPARQL endpoint, and we show that the algorithm is able to learn them, 82 % of them in less than a minute and asking the user just once.
How to cite:
ISO 690:
Potoniec, J. 2020. Learning SPARQL Queries from Expected Results. In Computing and Informatics, vol. 38, no.3, pp. 679-700. 1335-9150. DOI: https://doi.org/10.31577/cai_2019_3_679
APA:
Potoniec, J. (2020). Learning SPARQL Queries from Expected Results. Computing and Informatics, 38(3), 679-700. 1335-9150. DOI: https://doi.org/10.31577/cai_2019_3_679
About edition:
Publisher: Ústav informatiky SAV
Published: 9. 3. 2020