Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

T. Eiter, T. Krennwallner, P. Schneider:
"Lightweight Spatial Conjunctive Query Answering Using Keywords";
Vortrag: 10th Extended Semantic Web Conference (ESWC 2013), Montpellier, France; 26.05.2013 - 30.05.2013; in: "10th Extended Semantic Web Conference (ESWC 2013)", P. Cimiano, O. Corcho, V. Presutti, L. Hollink, S. Rudolph (Hrg.); Springer, 7882 (2013), ISBN: 978-3-642-38287-1; S. 243 - 258.



Kurzfassung englisch:
With the advent of publicly available geospatial data, ontology-based data access (OBDA) over spatial data has gained increasing interest. Spatio-relational DBMSs are used to implement geographic information systems (GIS) and are fit to manage large amounts of data and geographic objects such as points, lines, polygons, etc. In this paper, we extend the Description Logic DL-Lite with spatial objects and show how to answer spatial conjunctive queries (SCQs) over ontologies---that is, conjunctive queries with point-set topological relations such as next and within---expressed in this language. The goal of this extension is to enable an off-the-shelf use of spatio-relational DBMSs to answer SCQs using rewriting techniques, where data sources and geographic objects are stored in a database and spatial conjunctive queries are rewritten to SQL statements with spatial functions. Furthermore, we consider keyword-based querying over spatial OBDA data sources, and show how to map queries expressed as simple keyword lists describing objects of interest to SCQs, using a meta-model for completing the SCQs with spatial aspects. We have implemented our lightweight approach to spatial OBDA in a prototype and show initial experimental results using data sources such as Open Street Maps and Open Government Data Vienna from an associated project. We show that for real-world scenarios, practical queries are expressible under meta-model completion, and that query answering is computationally feasible.

Schlagworte:
Description Logics, Spatial Querying, Conjunctive Queries, Keyword-Based Queries


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1007/978-3-642-38288-8_17



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Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.