SPARQL Query Recommendations by Example
In this demo paper, a SPARQL Query Recommendation Tool (called SQUIRE) based on query reformulation is presented. Based on three steps, Generalization, Specialization and Evaluation, SQUIRE implements the logic of reformulating a SPARQL query that is satisfiable w.r.t a source RDF dataset, into others that are satisfiable w.r.t a target RDF dataset. In contrast with existing approaches, SQUIRE aims at recommending queries whose reformulations: (i) reflect as much as possible the same intended meaning, structure, type of results and result size as the original query and (ii) do not require to have a mapping between the two datasets. Based on a set of criteria to measure the similarity between the initial query and the recommended ones, SQUIRE demonstrates the feasibility of the underlying query reformulation process, ranks appropriately the recommended queries, and offers a valuable support for query recommendations over an unknown and unmapped target RDF dataset, not only assisting the user in learning the data model and content of an RDF dataset, but also supporting its use without requiring the user to have intrinsic knowledge of the data.
This work was supported by the MK:Smart project (OU Reference HGCK B4466).
- 1.Borsje, J., Embregts, H.: Graphical Query Composition and Natural Language Processing in an RDF Visualization Interface. E.S. of E. and B., Univ., Rott. (2006)Google Scholar
- 2.Correndo, G., Salvadores, M., Millard, I., Glaser, H., Shadbolt, N.: SPARQL query rewriting for implementing data integration over linked data. In: Proceedings of the EDBT/ICDT Workshops, EDBT 2010. ACM, New York (2010)Google Scholar
- 3.d’Aquin, M., Motta, E.: Extracting relevant questions to an RDF dataset using formal concept analysis. In Proceedings of the 6th K-CAP, USA (2011)Google Scholar
- 4.Ferre, S., Sparklis: an expressive query builder for SPARQL endpoints with guidance in natural language. Sem. Web Inter. Usab. App. (2016, to appear)Google Scholar
- 6.Hogenboom, F., Milea, V., Frasincar, F., Kaymak, U: RDF-GL: a SPARQL-based graphical query language for RDF. In: Emergent Web Intelligence: Advanced Information Retrieval (2010)Google Scholar
- 7.Makris, K., Bikakis, N., Gioldasis, N., Tsinaraki, C., Christodoulakis, S.: Towards a mediator based on OWL and SPARQL. In: Lytras, M.D., et al. (eds.) WSKS 2009. LNCS, vol. 5736, pp. 326–335. Springer, Heidelberg (2009)Google Scholar
- 8.Picalausa, F., Vansummeren, S.: What are real SPARQL queries like? In: Proceedings of SWIM 2011, pp. 7:1-7:6. ACM, New York (2011)Google Scholar
- 9.Reddy, B.R.K., Kumar, P.S.: Efficient approximate SPARQL querying of web of linked data. In: URSW CEUR Workshop Proceeding, CEUR-WS.org (2010)
- 10.Seneviratne, O.: QueryMed: an intuitive SPARQL query builder for biomedical RDF data (2010)Google Scholar