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A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures / Gangothri Rajaram in Geoinformatica, vol 22 n° 2 (April 2018)  

Public [article]  inGeoinformatica > vol 22 n° 2 (April 2018) . - pp 269 - 305 Titre : A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures Type de document : Article/Communication Auteurs : Gangothri Rajaram, Auteur ; Harish Chandra Karnatak, Auteur ; Swaminathan Venkatraman, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 269 - 305 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] cadre conceptuel [Termes IGN] métadonnées [Termes IGN] métadonnées géographiques [Termes IGN] pondération [Termes IGN] qualité des metadonnées [Termes IGN] service web Résumé : (Auteur) Advances in Metadata research have been instrumental in predictions and `fitness-of-use evaluation' for the effective Decision-making process. For the past two decades, the model has been developed to provide visual assistance for assessing the quality information in metadata and quantifying the degree of metadata population. Still, there is a need to develop a framework that can be generic to adopt all the standards available for Geospatial Metadata. The computational analysis of metadata for specific applications remains uncharted for investigations and studies. This work proposes a computational framework for Geospatial Metadata by integrating TopicMaps and Hypergraphs (HXTM) based on the elements and their dependency relationships. A purpose-built dataset extracted from schemas of various standardisation organisations and existing knowledge in the discipline is utilised to model the framework and thereby evaluate ranking strategies. Hypergraph-Helly Property based Weight-Assignment Algorithm (HHWA) have been proposed for HXTM framework to calculate Stable weights for Metadata Elements. Recursive use of Helly-property ensures predominant elements, while Rank Order Centroid (ROC) method is used to compute standard weights. A real corpus using case studies from FGDC's Standard for Geospatial Metadata, INSPIRE Metadata Standards, and ISRO Metadata Content Standard (NSDI 2.0) is used to validate the proposed framework. The observations show that the Information Gain (Entropy) of the proposed model along with the algorithm proves to be computationally smart for quantification purposes and visualises the strength of Metadata Elements for all applications. A prototype tool, `MetDEVViz- MetaData Editor, Validator &Visualization' is designed to exploit the benefits of the proposed algorithm for the case studies that acts as a web service to provide a user interface for editing, validating and visualizing metadata elements. Numéro de notice : A2018-365 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-018-0317-6 Date de publication en ligne : 28/02/2018 En ligne : https://doi.org/10.1007/s10707-018-0317-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90730 [article]