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From Georeferenced Data to Socio-Spatial Knowledge. Ontology Design Patterns to Discover Domain-Specific Knowledge from Crowdsourced Data

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Abstract

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.

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Notes

  1. 1.

    Note that: all the object properties depicted in the diagram have their inverse; we reused Dolce Lite Plus (prefix DLP) and a Time pattern (prefix Time).

  2. 2.

    The owl files are available here: https://gitlab.com/misplaced/urbis.git.

  3. 3.

    Note that the design of an urban artefact may also be informally encoded by members of the community; this is the case of informal settlements or markets which reflect certain typologies of material arrangement but they are not the result of a formal process of construction.

  4. 4.

    https://inspire.ec.europa.eu/Themes/126/2892.

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Author information

Authors and Affiliations

  1. University of Torino, Turin, Italy

    Alessia Calafiore & Guido Boella

  2. University of Luxembourg, Luxembourg City, Luxembourg

    Alessia Calafiore & Leender van der Torre

Authors
  1. Alessia Calafiore
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  2. Guido Boella
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  3. Leender van der Torre
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Corresponding author

Correspondence to Alessia Calafiore .

Editor information

Editors and Affiliations

  1. Université Côte d’Azur, CNRS, Inria, I3S, Sophia Antipolis, France

    Catherine Faron Zucker

  2. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  3. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Amedeo Napoli

  4. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Yannick Toussaint

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Calafiore, A., Boella, G., van der Torre, L. (2018). From Georeferenced Data to Socio-Spatial Knowledge. Ontology Design Patterns to Discover Domain-Specific Knowledge from Crowdsourced Data. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds) Knowledge Engineering and Knowledge Management. EKAW 2018. Lecture Notes in Computer Science(), vol 11313. Springer, Cham. https://doi.org/10.1007/978-3-030-03667-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-03667-6_3

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