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Spatially oriented convolutional neural network for spatial relation extraction from natural language texts / Qinjun Qiu in Transactions in GIS, vol 26 n° 2 (April 2022)
Titre : Spatially oriented convolutional neural network for spatial relation extraction from natural language texts Type de document : Article/Communication Auteurs : Qinjun Qiu, Auteur ; Zhong Xie, Auteur ; Kai Ma, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 839 - 866 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] appariement sémantique
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] exploration de données
[Termes IGN] langage naturel (informatique)
[Termes IGN] proximité sémantique
[Termes IGN] relation spatiale
[Termes IGN] relation topologique
[Termes IGN] site wiki
[Termes IGN] spatial metrics
[Termes IGN] système à base de connaissancesRésumé : (auteur) Spatial relation extraction (e.g., topological relations, directional relations, and distance relations) from natural language descriptions is a fundamental but challenging task in several practical applications. Current state-of-the-art methods rely on rule-based metrics, either those specifically developed for extracting spatial relations or those integrated in methods that combine multiple metrics. However, these methods all rely on developed rules and do not effectively capture the characteristics of natural language spatial relations because the descriptions may be heterogeneous and vague and may be context sparse. In this article, we present a spatially oriented piecewise convolutional neural network (SP-CNN) that is specifically designed with these linguistic issues in mind. Our method extends a general piecewise convolutional neural network with a set of improvements designed to tackle the task of spatial relation extraction. We also propose an automated workflow for generating training datasets by integrating new sentences with those in a knowledge base, based on string similarity and semantic similarity, and then transforming the sentences into training data. We exploit a spatially oriented channel that uses prior human knowledge to automatically match words and understand the linguistic clues to spatial relations, finally leading to an extraction decision. We present both the qualitative and quantitative performance of the proposed methodology using a large dataset collected from Wikipedia. The experimental results demonstrate that the SP-CNN, with its supervised machine learning, can significantly outperform current state-of-the-art methods on constructed datasets. Numéro de notice : A2022-365 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12887 Date de publication en ligne : 27/12/2021 En ligne : https://doi.org/10.1111/tgis.12887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100584
in Transactions in GIS > vol 26 n° 2 (April 2022) . - pp 839 - 866[article]
Titre : BuyTheDips : PathLoss for improved topology-preserving deep learning-based image segmentation Type de document : Article/Communication Auteurs : Minh On Vu Ngoc, Auteur ; Yizi Chen Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Projets : SODUCO / Perret, Julien Importance : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] chemin le plus court, algorithme du
[Termes IGN] fonction de perte
[Termes IGN] image numérique
[Termes IGN] proximité sémantique
[Termes IGN] segmentation d'imageRésumé : (auteur) Capturing the global topology of an image is essential for proposing an accurate segmentation of its domain. However, most of existing segmentation methods do not preserve the initial topology of the given input, which is detrimental for numerous downstream object-based tasks. This is all the more true for deep learning models which most work at local scales. In this paper, we propose a new topology-preserving deep image segmentation method which relies on a new leakage loss: the Pathloss. Our method is an extension of the BALoss [1], in which we want to improve the leakage detection for better recovering the closeness property of the image segmentation. This loss allows us to correctly localize and fix the critical points (a leakage in the boundaries) that could occur in the predictions, and is based on a shortest-path search algorithm. This way, loss minimization enforces connectivity only where it is necessary and finally provides a good localization of the boundaries of the objects in the image. Moreover, according to our research, our Pathloss learns to preserve stronger elongated structure compared to methods without using topology-preserving loss. Training with our topological loss function, our method outperforms state-of-the-art topology-aware methods on two representative datasets of different natures: Electron Microscopy and Historical Map. Numéro de notice : P2022-005 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2207.11446 En ligne : https://doi.org/10.48550/arXiv.2207.11446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101338
Titre : Vague spatio-thematic query processing: a qualitative approach to spatial closeness Type de document : Article/Communication Auteurs : R. Grütter, Auteur ; T. Scharrenbach, Auteur ; B. Waldvogel, Auteur Année de publication : 2010 Article en page(s) : pp 97 - 109 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] connaissance thématique
[Termes IGN] efficacité
[Termes IGN] OWL
[Termes IGN] proximité sémantique
[Termes IGN] requête spatiale
[Termes IGN] web sémantiqueRésumé : (Auteur) In order to support the processing of qualitative spatial queries, spatial knowledge must be represented in a way that machines can make use of it. Ontologies typically represent thematic knowledge. Enhancing them with spatial knowledge is still a challenge. In this article, an implementation of the Region Connection Calculus (RCC) in the Web Ontology Language (OWL), augmented by DL-safe SWRL rules, is used to represent spatio-thematic knowledge. This involves partially ordered partitions, which are implemented by nominals and functional roles. Accordingly, a spatial division into administrative regions, rather than, for instance, a metric system, is used as a frame of reference for evaluating closeness. Hence, closeness is evaluated purely according to qualitative criteria. Colloquial descriptions typically involve qualitative concepts. The approach presented here is thus expected to align better with the way human beings deal with closeness than does a quantitative approach. To illustrate the approach, it is applied to the retrieval of documents from the database of the Datacenter Nature and Landscape (DNL). Numéro de notice : A2010-200 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2010.01185.x Date de publication en ligne : 15/04/2010 En ligne : https://doi.org/10.1111/j.1467-9671.2010.01185.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30394
in Transactions in GIS > vol 14 n° 2 (April 2010) . - pp 97 - 109[article]Plate-forme AFIA, Nice, 30 mai - 3 juin 2005, 5. Posters IC 2005, 16es Journées Francophones Ingénierie des Connaissances, 30 mai - 3 juin 2005, Nice, France / Marie-Christine Jaulent (2005)
Voir aussi
- 7e conférence francophone sur l'apprentissage automatique, CAp 2005, [Plate-forme AFIA], 30 mai - 3 juin 2005, Nice, France / François Denis (2005)
- Plate-forme AFIA, Nice, 30 mai - 3 juin 2005, 4. Atelier Raisonnement à partir de cas / Sylvie Desprès (2005)
- 7es Rencontres des Jeunes Chercheurs en Intelligence Artificielle [Plate-forme AFIA 2005] / Emmanuel Guéré (2005)
- Plate-forme AFIA, Nice, 30 mai - 3 juin 2005, 1. Journéee thématique Web sémantique pour le e-learning / Rose Dieng-Kuntz (2005)
- Plate-forme AFIA, Nice, 30 mai - 3 juin 2005, 2. Journée thématique Raisonner le web sémantique avec des graphes / Michel Leclère (2005)
- Plate-forme AFIA, Nice, 30 mai - 3 juin 2005, 3. Atelier Connaissance et documents temporels / Yannick Prié (2005)
Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 24820-01E CG2005 Livre Centre de documentation Congrès Disponible
contenu dans Advances in GIS Research 2 : proceedings SDH'96, Volume 1. Proceedings of the 7th International symposium on spatial data handling SDH'96 / Menno-Jan Kraak (1996)
Titre : Conceptual, spatial and temporal referencing of multimedia objects Type de document : Article/Communication Auteurs : Christopher B. Jones, Auteur ; C. Taylor, Auteur ; D. Tudhope, Auteur ; P. Beynon-Davies, Auteur Editeur : Delhi, Washington, Delft... : International Geographical Union IGU Année de publication : 12/08/1996 Conférence : SDH 1996, 7th International symposium on spatial data handling 12/08/1996 16/08/1996 Delft Pays-Bas OA proceedings Importance : 12 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique
[Termes IGN] données localisées
[Termes IGN] données spatiotemporelles
[Termes IGN] information géographique
[Termes IGN] information sémantique
[Termes IGN] modélisation
[Termes IGN] multimedia
[Termes IGN] proximité sémantique
[Termes IGN] requête spatiale
[Termes IGN] réseau sémantique
[Termes IGN] système d'information géographiqueRésumé : (Auteur) The increasing usage in GIS of multimedia objects, such as scanned photographs and video recordings, has introduced the need to provide flexible indexing methods to help in accessing objects that may be of relevance to a range of possible applications. An experimental system is described in which media objects may be referenced to several classifications concepts, as well as qualitative descriptions of geographic location and time period. Semantic modelling techniques are used to create semantic networks that represent the hierarchical and lattice structures of conceptual, temporal and geographic space. The associated query methods enable inexact matching between user interests and the media object descriptors. Several metrics of semantic closeness, in concept, time and geography have been developped, based on scaled measures of path lengths within the semantic networks that represent the three dimensions of indexing. Numéro de notice : C1996-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=65641
