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A graph-based approach for representing addresses in geocoding / Chen Zhang in Computers, Environment and Urban Systems, vol 100 (March 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101937 Titre : A graph-based approach for representing addresses in geocoding Type de document : Article/Communication Auteurs : Chen Zhang, Auteur ; Biao He, Auteur ; Renzhong Guo, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101937 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] appariement d'adresses [Termes IGN] base de données d'adresses [Termes IGN] géocodage par adresse postale [Termes IGN] graphe [Termes IGN] stockage de données [Termes IGN] toponyme Résumé : (auteur) Addresses, one of the most important geographical reference systems in natural languages, are usually used to search spatial objects in daily life. Geocoding concatenates text with georeferenced coordinates and is an essential middleware service in geographic information applications. Despite its importance, geocoding remains challenging with only text as input, hindering text matching in reference databases without the specific text. To optimize the storage and retrieval of addresses in databases, this work proposes a graph-based approach for representing addresses. The approach clarifies the characteristics of relative concepts, designs a graph structure and identifies modelling strategies. Furthermore, a schema is proposed to perform address matching and toponym disambiguation using an address graph. The model is implemented on a graph database, and experimental tasks are employed to demonstrate its effectiveness. The approach provides a new reference for developers when creating address databases. Numéro de notice : A2023-126 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101937 Date de publication en ligne : 04/01/2023 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101937 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102505 [article]

A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 1 (February 2021) . - pp 359 - 381 Titre : A points of interest matching method using a multivariate weighting function with gradient descent optimization Type de document : Article/Communication Auteurs : Zhou Yang, Auteur ; Mingjun Wang, Auteur ; Chen Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 359 - 381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] algorithme du gradient [Termes IGN] appariement automatique [Termes IGN] appariement de données localisées [Termes IGN] apprentissage automatique [Termes IGN] données localisées des bénévoles [Termes IGN] données multisources [Termes IGN] exploration de données [Termes IGN] intégration de données [Termes IGN] point d'intérêt [Termes IGN] pondération [Termes IGN] qualité des données Résumé : (Auteur) Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning. Numéro de notice : A2021-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12690 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1111/tgis.12690 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97158 [article]