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Semantic querying of integrated raster and relational data: a virtual knowledge graph approach
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0003-0632-0294
Umeå University, Faculty of Science and Technology, Department of Computing Science. Research Centre for Knowledge and Data, Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.ORCID iD: 0000-0001-5174-9693
2023 (English)In: Proceedings of the 17th International Rule Challenge and 7th Doctoral Consortium @ RuleML+RR 2023 (RuleML+RR-Companion 2023), Oslo, Norway, 18 - 20 September, 2023 / [ed] Jan Vanthienen; Tomáš Kliegr; Paul Fodor; Davide Lanti; Dörthe Arndt; Egor V. Kostylev; Theodoros Mitsikas; Ahmet Soylu, CEUR-WS , 2023, article id 8240Conference paper, Published paper (Refereed)
Abstract [en]

Ontology-based data access (OBDA) facilitates access to heterogeneous data sources through the mediation of an ontology (e.g. OWL), which captures the domain of interest and is connected to data sources through a declarative mapping. In our study, large, heterogeneous earth observational (EO) data, known as raster data, and geometrical data, known as vector data, are considered as (heterogeneous) data sources. Raster data represent, e.g., Earth's natural phenomena, such as surface temperature, elevation, or air pollution, as multidimensional arrays. In contrast, vector data depict, e.g., locations, networks, or regions on Earth, using geometries. Domain experts, such as earth scientists and GIS practitioners, still struggle to undertake advanced studies by querying large raster and vector data in an integrated way because, unlike relational data, they come in diverse formats and different data structures. In our approach to integration, we use a geospatial extension of an RDBMS to represent vector data as relational data, and a domain-agnostic array DBMS to handle raster data. Our aim is to extend the OBDA paradigm to effectively deal with relational, vector, and raster data in a combined way, while leveraging the built-in capabilities of data management tools relevant to each type of data. We also plan to develop techniques to calculate on the fly for each user query posed over the ontology an optimal query plan that exploits, at best, the query processing capabilities of each tool, while limiting costly data transfer operations between tools.

Place, publisher, year, edition, pages
CEUR-WS , 2023. article id 8240
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 3485
Keywords [en]
Artificial Intelligence (AI), Knowledge Representation, Multi-Dimensional Arrays, Ontology-Based Data Access (OBDA), Raster Data, Relational Data, Spatial-temporal reasoning, Vector Data, Virtual Knowledge Graph (VKG)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-215876Scopus ID: 2-s2.0-85174146079OAI: oai:DiVA.org:umu-215876DiVA, id: diva2:1809764
Conference
17th International Rule Challenge and 7th Doctoral Consortium @ RuleM+RR, RuleML+RR-Companion 2023, Oslo, September 18-20, 2023
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2023-11-06 Created: 2023-11-06 Last updated: 2023-11-06Bibliographically approved

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Ghosh, ArkaŠimkus, MantasCalvanese, Diego

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CiteExportLink to record
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