AUTOMATIC ONTOLOGY GENERATION OF BIM AND GIS DATA
Data represented in the form of geospatial context and detailed building information are prominently nurturing infrastructure development and smart city applications. Bringing open-formats from data acquisition level to information engineering accelerates geospatial technologies towards urban sustai...
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Sprache: | eng |
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Zusammenfassung: | Data represented in the form of geospatial context and detailed building information are prominently nurturing infrastructure development and smart city applications. Bringing open-formats from data acquisition level to information engineering accelerates geospatial technologies towards urban sustainability and knowledge-based systems. BIM and GIS technologies are known to excel in this domain. However, fundamental level differences lie among their data-formats, which developed integration methods to bridge the gap between these distinct domains. Several studies have conducted data, process, and application-level integration, considering the significance of collaboration among these information systems. Although integration methods have narrowed the gap of geometric dissimilarity, semantic inconsistency, and information loss yet add constraints towards achieving interoperability. Integration using semantic web technology is more flexible and enables process-level integration without changing data format and structure. However, due to its developing nature and complex BIM-GIS data-formats, most approaches adapted requires human intervention. This paper presents a method, named OGGD (Ontology Generation for Geospatial Data), that implements a formal method for automatic ontology generation from XSD documents using transformation patterns following three extensive processes; first, formalization of XSD elements and transformation patterns; the second process identifies corresponding patterns explicitly, and the last process generates ontology for XSD schema. XSD elements from open-standard data models of BIM and GIS, ifcXML and CityGML, are manipulated and transformed into a semantically rich OWL model. The ontology models created can be applicable for information-based integration systems that will nurture knowledge-discovery and urban applications. |
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ISSN: | 2194-9034 1682-1750 2194-9034 |
DOI: | 10.5194/isprs-archives-XLIII-B4-2020-77-2020 |