McGlinn, Kris ORCID: 0000-0002-7023-5169, O’Sullivan, Declan ORCID: 0000-0003-1090-3548, Debruyne, Christophe ORCID: 0000-0003-4734-3847, Clinton, Éamonn and Brennan, Rob ORCID: 0000-0001-8236-362X (2020) Geoff: a linked data vocabulary for describing the form and function of spatial objects. In: 28th Irish Conference on Artificial Intelligence and Cognitive Science, 7-8 Dec 2020, Dublin, Ireland (Online).
Abstract
Geoff, the geospatial form and function vocabulary, is a comprehensive RDF-based spatial object classification scheme based on a separation of the
concepts of form and function. Geoff is based on our analysis of the extensive
(over 50 million spatial object instances) Digital Landscape Model (DLM) Core
model maintained by Ordnance Survey Ireland (OSi). We propose Geoff as there
are currently no open geospatial form and function classification systems that
cover the full range of geospatial objects (from buildings and roads to lakes and
other natural features) modelled as Linked Data or in any other formalism. Geoff
is a generalization of the DLM Core schema and adopts the GeoSPARQL ontology. Geoff was initially developed to make these classifications available for
OSi’s geospatial Linked Data as they facilitate the publications of more expressive models of spatial features. For example, to state that a church building (form)
is now used as apartments (function). Geoff is now presented to the wider community for reuse and extension to meet their own needs. Geoff supports geospatial queries based on form and function and interlinking of geo-information datasets using different form and function code lists. The Geoff ontology follows
Linked Data publishing best practice in terms of available metadata, documentation, and quality assurance.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | Ontology; Form and Function; Geospatial Feature |
Subjects: | Computer Science > Artificial intelligence |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Initiatives and Centres > ADAPT |
Published in: | Proceedings of the 28th Irish Conference on Artificial Intelligence and Cognitive Science. . |
Official URL: | http://ceur-ws.org/Vol-2771/AICS2020_paper_11.pdf |
Copyright Information: | © 2020 The Authors |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | Ordnance Survey Ireland and the ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded by the European Regional Development Fund |
ID Code: | 25389 |
Deposited On: | 18 Jan 2021 12:50 by Vidatum Academic . Last Modified 12 Mar 2021 12:39 |
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