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Consistency assessment for open geodata integration: an ontology-based approach / Linfang Ding in Geoinformatica, vol 25 n° 4 (October 2021)  

Public [article]  inGeoinformatica > vol 25 n° 4 (October 2021) . - pp 733 - 758 Titre : Consistency assessment for open geodata integration: an ontology-based approach Type de document : Article/Communication Auteurs : Linfang Ding, Auteur ; Guohui Xiao, Auteur ; Diego Calvanese, Auteur ; Liqiu Meng, Auteur Année de publication : 2021 Article en page(s) : pp 733 - 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] cadre conceptuel [Termes IGN] cohérence des données [Termes IGN] données hétérogènes [Termes IGN] données localisées [Termes IGN] données ouvertes [Termes IGN] intégration de données [Termes IGN] Italie [Termes IGN] ontologie Résumé : (auteur) Integrating heterogeneous geospatial data sources is important in various domains like smart cities, urban planning and governance, but remains a challenging research problem. In particular, the production of high-quality integrated data from multiple sources requires an understanding of their respective characteristics and a systematic assessment of the consistency within and between the data sources. In order to perform the assessment, the data has to be placed on a common ground. However, in practice, heterogeneous geodata are often provided in diverse formats and organized in significantly different structures. In this work, we propose a framework that uses an ontology-based approach to overcome the heterogeneity by means of a domain ontology, so that consistency rules can be evaluated at the unified ontological representation of the data sources. In our case study, we use open governmental data from Open Data Portals (ODPs) and volunteered geographic information from OpenStreetMap (OSM) as two test data sources in the area of the province of South Tyrol, Italy. Our preliminary experiment shows that the approach is effective in detecting inconsistencies within and between ODP and OSM data. These findings provide valuable insights for a better combined usage of these datasets. Numéro de notice : A2021-967 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-019-00384-9 Date de publication en ligne : 03/12/2019 En ligne : https://doi.org/10.1007/s10707-019-00384-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100388 [article]