Abstract
We describe and demonstrate a prototype of a UML-style visual query environment over DBPedia that allows query seeding with any class or property present in the data endpoint and provides for context-sensitive query growing based on class-to-property and property-to-property mappings. To handle mappings that connect more than 480 thousand classes and more than 50 thousand properties, a hybrid approach of mapping pre-computation and storage is proposed, where the property information for “large” classes is stored in a database, while for “small” classes and for individuals the matching property information is retrieved from the data endpoint on-the-fly. The created schema information is used to back the query seeding and growing in the ViziQuer tool. The schema server and the schema database contents can be re-used also in other applications that require DBPedia class and property linking information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
Even if the query has a more complicated structure, the completion suggestions are computed on the basis of the described simple node-edge model.
- 6.
The same applies also to query building in other (e.g.textual) notations.
- 7.
For DBPedia core the direct property-property relation is much smaller than the property-property relation derived from the property-class-property mappings. For endpoints with less subclassing and the class structure more fully representing the property availability, the property-property mapping derived from the property-class-property relation may be sufficient.
- 8.
- 9.
A property p is outgoing (resp., incoming) for a class c, if there is a c instance that is subject (resp., object) for some triple having p as its property.
- 10.
In the case of a heterogeneous endpoint, as DBPedia core is, the computation of local frequency of target instances in a context can give substantially different results from looking at the global “size” of the target entity.
- 11.
- 12.
if a class c corresponds to both a property p and a property q, it is going to be suggested in a context of both p and q, although there may be no instance of c with values for both p and q.
- 13.
There are about 35 million rows in the class-to-property (outgoing) relation in DBPedia core; the class-to-property (incoming) relation is much smaller.
- 14.
Within our initial prototype version, the class-to-property mapping is pre-computed for top 3000 largest classes; these classes contain at least about 1000 instances each.
- 15.
In the case of the DBPedia core endpoint the size of such a list for classes with less than 1000 instances typically do not exceed a few hundred.
- 16.
- 17.
The DBPedia ontology covers just a tiny fraction of the actual DBPedia core data structure.
- 18.
The data owner or a person having access to the data dump can also have other options of producing the data schema.
- 19.
- 20.
this requires setting up a local DBPedia instance to enable queries with 500K result set, split e.g., in chunks of 100K, we order the classes by their instance count descending.
- 21.
currently, the 3000 largest classes; the class count, or size threshold is introduced by the user; the optimal level of the threshold can be discussed.
- 22.
we did the detailed computations automatically for classes larger than 500K instances.
- 23.
- 24.
From the explicitly stated ontology and the sub-class-of assertions in the main data graph.
References
Bizer, C., et al.: “DBpedia-a crystallization point for the Web of Data” (PDF). Web Semant. Sci. Services Agents World Wide Web 7(3), 154–165 (2009)
Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)
Vargas, H., Buil-Aranda, C., Hogan, A., López, C.: RDF Explorer: A Visual SPARQL Query Builder. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 647–663. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_37
Ferré, S.: Sparklis: an expressive query builder for SPARQL endpoints with guidance in natural language. Semant. Web 8, 405–418 (2017)
Soylu, A., Giese, M., Jimenez-Ruiz, E., Vega-Gorgojo, G., Horrocks, I.: Experiencing OptiqueVQS: a Multi-paradigm and ontology-based visual query system for end users. Univ. Access Inf. Soc. 15(1), 129–152 (2016)
Klungre, V.N., Soylu, A., Jimenez-Ruiz, E., Kharlamov, E., Giese, M.: Query extension suggestions for visual query systems through ontology projection and indexing. N. Gener. Comput. 37(4), 361–392 (2019). https://doi.org/10.1007/s00354-019-00071-1
Čerāns, K., et al.: ViziQuer: A Web-Based Tool for Visual Diagrammatic Queries Over RDF Data. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 11155, pp. 158–163. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98192-5_30
Čerāns, K., et al.: Extended UML class diagram constructs for visual SPARQL queries in ViziQuer/web In Voila!2017. CEUR Workshop Proceed. 1947, 87–98 (2017)
YASGUI. https://yasgui.triply.cc/
Kapourani, B., Fotopoulou, E., Papaspyros, D., Zafeiropoulos, A., Mouzakitis, S., Koussouris, S.: Propelling SMEs Business Intelligence Through Linked Data Production and Consumption. In: Ciuciu, I., et al. (eds.) OTM 2015. LNCS, vol. 9416, pp. 107–116. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26138-6_14
Čerāns, K., et al.: ViziQuer: a Visual notation for RDF data analysis queries. In: Garoufallou, E., Sartori, F., Siatri, R., Zervas, M. (eds.) Metadata and Semantic Research. CCIS, vol. 846. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14401-2_5
Dudáš, M., Svátek, V., Mynarz, J.: Dataset summary visualization with LODSight. In: The 12th Extended Semantic Web Conference (ESWC2015)
Čerāns, K., Ovčiņņikova, J., Bojārs, U., Grasmanis, M., Lāce, L., Romāne, A.: Schema-Backed Visual Queries over Europeana and Other Linked Data Resources. In: Verborgh, R., et al. (eds.) ESWC 2021. LNCS, vol. 12739, pp. 82–87. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80418-3_15
Acknowledgements
This work has been partially supported by a Latvian Science Council Grant lzp-2020/2-0188 “Visual Ontology-Based Queries”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Čerāns, K., Lāce, L., Grasmanis, M., Ovčiņņikova, J. (2022). A UML-Style Visual Query Environment Over DBPedia. In: Garoufallou, E., Ovalle-Perandones, MA., Vlachidis, A. (eds) Metadata and Semantic Research. MTSR 2021. Communications in Computer and Information Science, vol 1537. Springer, Cham. https://doi.org/10.1007/978-3-030-98876-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-98876-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98875-3
Online ISBN: 978-3-030-98876-0
eBook Packages: Computer ScienceComputer Science (R0)