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5 results on '"K-City Labs - Kinaxia Group"'

1. Towards a representation of uncertain geospatial information in knowledge graphs

Author

Cadorel, Lucie, Tettamanzi, Andrea G. B., Gandon, Fabien, Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), K-City Labs - Kinaxia Group, ACM, and ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)

Subjects
Fuzzy Sets, Knowledge Graph, Uncertainty, Uncertainty Fuzzy Sets Knowledge Graph, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; This paper highlights the challenges of representing uncertain geospatial information in knowledge graphs. We propose to use Real Estate advertisements since professionals use a lot of vernacular and vague places in order to promote a house to their target audience. Then, we suggest to model local place names using fuzzy set theory. Finally, we discuss how to build a knowledge graph that represents extracted geospatial objects and their uncertainty. CCS CONCEPTS • Information systems → Geographic information systems.

Published
2022

2. Fuzzy representation of vague spatial descriptions in real estate advertisements

Author

Cadorel, Lucie, Overal, Denis, Tettamanzi, Andrea G. B., Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), K-City Labs - Kinaxia Group, and ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)

Subjects
CCS CONCEPTS Information systems → Information retrieval, Fuzzy sets, Geocoding, Geographic information systems Uncertainty, Geographic information systems Uncertainty Fuzzy sets Spatial relationships Natural Language Geocoding, Information systems → Information retrieval, Spatial relationships, Natural Language, CCS CONCEPTS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Geocoding a spatial description is challenging since vernacular place names and vague spatial expressions give uncertainty and ambiguity to the description. Usually, digital gazetteers are used to match geospatial objects to their boundaries. However, gazetteers do not contain all places. Therefore, a number of studies have proposed to enrich gazetteers by estimating and representing the vernacular places. Nevertheless, only a few approaches have taken into account vague spatial expressions such as "nearby", and have represented geospatial objects as sharp boundaries. In this work, we present an automatic workflow to retrieve a location approximation of vague spatial description. We propose a model to estimate a fuzzy representation of each mentioned geospatial information and spatial expressions. Then, we perform information fusion to find a location approximation of a property. Lastly, we demonstrate our proposed method by applying it to the case of French Real Estate advertisements with two real-world datasets in Nice and Paris. Real Estate advertisements allow us to deal with uncertain geospatial objects since a vague and exaggerated property location's description is usually provided. Our results show that our proposed method is promising and able to correctly approximate a location from uncertain spatial descriptions.

Published
2022

3. Connaissances géospatiales dans les annonces immobilières : détection et extraction d’information spatiale à partir du texte

Author

Cadorel, Lucie, Blanchi, Alicia, Tettamanzi, Andrea G. B., Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), K-City Labs - Kinaxia Group, Expertise et spatialisation des connaissances en environnement (ESPACE), Institut de Recherche pour le Développement (IRD), AfIA, Fatiha Saïs, and ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)

Subjects
geographical knowledge, Information extraction, connaissance géographique, named entity recognition, extraction de relation, reconnaissance d’entités nommées, Extraction d’information, relationship extraction, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; We proposed a workflow to extract geospatial knowledge from text applied to Real Estate advertisements. We first extracted geographic and spatial entities using a model based on a BiLSTM-CRF architecture with a concatenation of se-veral text representations. Secondly, we performed relations extraction, particularly spatial relations extraction, to build a structured Geospatial knowledge base that we stored in a RDF Knowledge Graph.; Nous avons proposé un modèle d’extraction de connaissances géospatiales à parir du texte appliqué au cas des annonces immobilières. La première étape consiste à extraire les entités géographiques et spatiales à l’aide d’un modèlebasé sur une architecture BiLSTM-CRF et la concaténation de plusieurs embeddings. Ensuite, nous avons réalisé l’extraction de relations, notamment spatiales, pour créer une base de connaissance géospatiale structurée stockéedans un graphe de connaissance RDF.

Published
2022

4. Possibilistic Estimation of Distributions to Leverage Sparse Data in Machine Learning

Author

Célia da Costa Pereira, Alessandro Venerandi, Giovanni Fusco, Andrea G. B. Tettamanzi, David Emsellem, Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), K-City Labs - Kinaxia Group, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), IDEX 'UCA JEDI', AAP Partenariat 2016 (action 6.5), Marie-Jeanne Lesot, Susana M. Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager, UCA JEDI, ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Lesot, Marie-Jeanne, Vieira, Susana, Reformat, Marek Z., Carvalho, João Paulo, Wilbik, Anna, Bouchon-Meunier, Bernadette, and Yager, Ronald R.

Subjects
QA75, Urban form, Computer science, business.industry, Supervised learning, 0211 other engineering and technologies, 02 engineering and technology, Machine learning, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, Weakly Supervised Learning, QA273, 0202 electrical engineering, electronic engineering, information engineering, Leverage (statistics), 020201 artificial intelligence & image processing, Artificial intelligence, Possibility Theory, Market value, business, computer, 021101 geological & geomatics engineering, Possibility theory, Sparse matrix
Abstract

International audience; Prompted by an application in the area of human geography using machine learning to study housing market valuation based on the urban form, we propose a method based on possibility theory to deal with sparse data, which can be combined with any machine learning method to approach weakly supervised learning problems. More specifically, the solution we propose constructs a possibilistic loss function to account for an uncertain supervisory signal. Although the proposal is illustrated on a specific application, its basic principles are general. The proposed method is then empirically validated on real-world data.

Published
2020

5. A Machine Learning Approach to Study the Relationship between Features of the Urban Environment and Street Value

Author

Alessandro Venerandi, Giovanni Fusco, Andrea G. B. Tettamanzi, David Emsellem, Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), K-City Labs - Kinaxia Group, ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Université Nice Sophia Antipolis (... - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS)

Subjects
Multivariate statistics, Computer science, 0211 other engineering and technologies, 0507 social and economic geography, Urban environment, 02 engineering and technology, Machine learning, computer.software_genre, Street value, French Riviera, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], lcsh:Social Sciences, 11. Sustainability, Architecture, Set (psychology), Socioeconomic status, Ensemble method, business.industry, 05 social sciences, lcsh:Geography. Anthropology. Recreation, 021107 urban & regional planning, General Medicine, [SHS.GEO]Humanities and Social Sciences/Geography, Unit of analysis, lcsh:H, lcsh:G, Independence (mathematical logic), NA, Artificial intelligence, business, 050703 geography, computer, Value (mathematics)
Abstract

Understanding what aspects of the urban environment are associated with bettersocioeconomic/liveability outcomes is a long standing research topic. Several quantitative studieshave investigated such relationships. However, most of such works analysed single correlations, thusfailing to obtain a more complete picture of how the urban environment can contribute to explainthe observed phenomena. More recently, multivariate models have been suggested. However, theyuse a limited set of metrics, propose a coarse spatial unit of analysis, and assume linearity andindependence among regressors. In this paper, we propose a quantitative methodology to studythe relationship between a more comprehensive set of metrics of the urban environment and thevalorisation of street segments that handles non-linearity and possible interactions among variables,through the use of Machine Learning (ML). The proposed methodology was tested on the FrenchRiviera and outputs show a moderate predictive capacity (i.e., adjusted R2 = 0.75) and insightfulexplanations on the nuanced relationships between selected features of the urban environment andstreet values. These findings are clearly location specific, however, the methodology is replicable andcan thus inspire future research of this kind in different geographic contexts.

Published
2019

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