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