Development of Intelligent SPARQL Query Editor
https://doi.org/10.25205/1818-7900-2021-19-4-85-95
Abstract
The paper discusses the development of SPARQL query editor. This is an actual question because of the growth of Semantic Web data – the data presented in RDF/OWL formats. A comparative analysis of different types of editors and their main features is provided. In this paper, we propose a description of a SPARQL editor that combines three most useful features: intelligent completions, query visualization comparison of query results provided by different reasoners or without them. The editor provides SPARQL support as LSP service, this approach is considered a modern way to implement language support. This paper also presents the editor’s screenshots.
About the Authors
I. A. Turova
Perm State University
Russian Federation
Russian Federation
Irina A. Turova, Master’s Student
Perm
I. S. Postanogov
Perm State University
Russian Federation
Russian Federation
Igor S. Postanogov, Senior Lecturer
Perm
References
1. Dissanayake P. I., Colicchio T. K., Cimino J. J. Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis. Journal of the American Medical Informatics Association, 2020, vol. 27, no. 1, pp. 159–174. DOI 10.1093/jamia/ocz169
2. Rietveld L., Hoekstra R. Man vs machine: Differences in SPARQL queries. In: Proceedings of the 4th USEWOD Workshop on Usage Analysis and the Web of Data, ESWC, 2014.
3. Warren P., Mulholland P. Using SPARQL – the practitioners’ viewpoint. In: European Knowledge Acquisition Workshop. Cham., 2018, pp. 485–500. DOI 10.1007/978-3-030-03667-6_31
4. Vargas H., Buil-Aranda C., Hogan A., López C. RDF Explorer: A Visual SPARQL Query Builder. In: International Semantic Web Conference. Cham., 2019, pp. 647–663. DOI 10.1007/978-3-030-30793-6_37
5. Hogenboom F., Milea V., Frasincar F., Kaymak U. RDF-GL: A SPARQLBased Graphical Query Language for RDF. In: Emergent Web Intelligence: Advanced Information Retrieval, 2010, pp. 87–116. DOI 10.1007/978-1-84996-074-8_4
6. Smart R., Russell A., Braines D., Kalfoglou Y., Bao J., Shadbolt R. A Visual Approach to Semantic Query Design Using a Web-Based Graphical Query Designer. In: Knowledge Engineering and Knowledge Management (EKAW), 2008, pp. 275–291. DOI 10.1007/978-3-540-87696-0_25
7. Rietveld L., Hoekstra R. YASGUI: not just another SPARQL client. In: Extended Semantic Web Conference. Berlin, 2013, pp. 78–86. DOI 10.1007/978-3-642-41242-4_7
8. Rietveld L., Hoekstra R. The YASGUI family of SPARQL clients. Semantic Web, 2017, vol. 8, no. 3, pp. 373–383. DOI 10.3233/sw-150197
9. Campinas S., Perry T. E., Ceccarelli D., Delbru R., Tummarello G. Introducing RDF graph summary with application to assisted SPARQL formulation. In: 23rd International Workshop on Database and Expert Systems Applications, 2012, pp. 261–266. DOI 10.1109/dexa.2012.38
10. Gardiner T., Horrocks I., Tsarkov D. Automated benchmarking of description logic reasoners. In: Proceedings of the International Workshop on Description Logics (06) CEUR, 2006, vol. 189, pp. 167–174.
11. Alaya N., Yahia S.B., Lamolle M. RakSOR: Ranking of ontology reasoners based on predicted performances. In: IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016, pp. 1076–1083. DOI 10.1109/ictai.2016.0165
12. Rafes K., Abiteboul S., Cohen-Boulakia S., Rance B. Designing scientific SPARQL queries using autocompletion by snippets. In: IEEE 14th International Conference on e-Science, 2018, pp. 234–244. DOI 10.1109/escience.2018.00038
Review
For citations:
Turova I.A., Postanogov I.S. Development of Intelligent SPARQL Query Editor. Vestnik NSU. Series: Information Technologies. 2021;19(4):85-95. (In Russ.) https://doi.org/10.25205/1818-7900-2021-19-4-85-95
Views:
173
Email this article 