Ontology Design Pattern; Socio-spatial knowledge; Data mining; Crowdsourced data; Social behaviour
Résumé :
[en] So far, ontologies developed to support Geographic Information science have been mostly designed from a space-centered rather than a human-centered and social perspective. In the last decades, a wealth of georeferenced data is collected through sensors, mobile and web platforms from the crowd, providing rich information about people’s collective experiences and behaviors in cities. As a consequence, these new data sources require models able to make machine-understandable the social meanings and uses people commonly associate with certain places. This contribution proposes a set of reusable Ontology Design Patterns (ODP) to guide a data mining workflow and to semantically enrich the mined results. The ODPs explicitly aim at representing two facets of the geographic knowledge - the built environment and people social behavior in cities - as well as the way they interact. Modelling the interplay between the physical and the human aspects of the urban environment provides an ontology representation of the socio-spatial knowledge which can be used as baseline domain knowledge for analysing and interpreting georeferenced data collected through crowdsourcing. An experimentation using a TripAdvisor data sample to recognize food consumption practices in the city of Turin is presented.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Calafiore, Alessia
Boella, Guido
VAN DER TORRE, Leon ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
From Georeferenced Data to Socio-Spatial Knowledge. Ontology Design Patterns to Discover Domain-Specific Knowledge from Crowdsourced Data
Date de publication/diffusion :
2018
Nom de la manifestation :
INTERNATIONAL CONFERENCE ON KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT
Date de la manifestation :
12-16 Nov. 2018
Titre de l'ouvrage principal :
21st International Conference on Knowledge Engineering and Knowledge Management
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