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Geoscience Knowledge Graph (GeoKG): Development, construction and challenges / Xueying Zhang in Transactions in GIS, vol 26 n° 6 (September 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 6 (September 2022) . - pp 2480 - 2494 Titre : Geoscience Knowledge Graph (GeoKG): Development, construction and challenges Type de document : Article/Communication Auteurs : Xueying Zhang, Auteur ; Yi Huang, Auteur ; Chunju Zhang, Auteur ; Peng Ye, Auteur Année de publication : 2022 Article en page(s) : pp 2480 - 2494 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] corrélation [Termes IGN] données localisées numériques [Termes IGN] représentation des connaissances [Termes IGN] réseau sémantique Résumé : (auteur) Big earth data is a cross-domain of geoscience and information science, which provides a novel perspective for solving geoscience problems. Most contemporary research is driven by data but neglect the potential value of knowledge. As a new scientific language in Geoscience, GeoKG is essential for understanding, representing, and mining geoscience knowledge, and can contribute to the integration of big earth data, geoscience knowledge, and geoscience models. However, research on GeoKG lack spatiotemporal perspectives in knowledge cognition, representation, acquisition and management. To this end, this article first outlines a cognitive mechanism from the human–machine double perspective and categorizes the characteristics and content of geoscience knowledge. To express evolution and complex natural rules, a knowledge representation framework is proposed through ‘state-process’ and ‘condition-result’ models. Aiming at multimodal data, a workflow is put forward to acquire knowledge from a small sample, a knowledge graph, a map, and a schematic diagram. Furthermore, we discuss the organization of GeoKG by improving existing data models, developing spatiotemporal correlation indexing and advancing knowledge graph completion. The concrete construction process of GeoKG is analyzed thoroughly in this study, which can support the synthetic analysis of big earth data, promote the development of knowledge engineering and provide a foundation for improving intelligent geoscience. Numéro de notice : A2022-699 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1111/tgis.12985 En ligne : https://doi.org/10.1111/tgis.12985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102142 [article]

Geographic Knowledge Graph (GeoKG): A formalized geographic knowledge representation / Shu Wang in ISPRS International journal of geo-information, vol 8 n° 4 (April 2019)  

Public [article]  inISPRS International journal of geo-information > vol 8 n° 4 (April 2019) . - n° 184 Titre : Geographic Knowledge Graph (GeoKG): A formalized geographic knowledge representation Type de document : Article/Communication Auteurs : Shu Wang, Auteur ; Xueying Zhang, Auteur ; Peng Ye, Auteur ; Mi Du, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : n° 184 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques [Termes IGN] formalisation [Termes IGN] langage de programmation [Termes IGN] Nankin (Kiangsou) [Termes IGN] représentation des connaissances [Termes IGN] réseau sémantique Résumé : (auteur) Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation. Numéro de notice : A2019-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.3390/ijgi8040184 Date de publication en ligne : 08/04/2019 En ligne : https://doi.org/10.3390/ijgi8040184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100286 [article]

Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm / Xiaonan Wang in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 2 (February 2018) Titre : Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm Type de document : Article/Communication Auteurs : Xiaonan Wang, Auteur ; Shihong Du, Auteur ; Chen-Chieh Feng, Auteur ; Xueying Zhang, Auteur ; Xiuyuan Zhang, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] langage naturel (informatique) [Termes IGN] relation sémantique [Termes IGN] relation topologique [Termes IGN] toponyme flou Résumé : (Auteur) Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts. Numéro de notice : A2018-107 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7020058 En ligne : https://doi.org/10.3390/ijgi7020058 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89533 [article]