The open data community is becoming aware of the proliferation of data and its exponential growth in size related to various domains. Another important aspect of open data is diversity, where the focus lies on managing heterogeneous datasets. In this work, we propose a common data model for the geospatial domain to address the diversity of (open) geospatial data, which we name GeoEtypes. GeoEtypes has two components. The first is the GeoEtypes Schema which provides a unified schema, while the second, named GeoEtypes Voc, is an aggregation for the vocabulary of terms. The idea behind this vocabulary is to provide natural language description of all terms used in the schema to make the model self-sufficient. GeoEtypes is constituted as a formalisation of INSPIRE, the European directive on spatial data. The model has been evaluated on three global datasets as well as on a local dataset with the main purpose to validate its adaptability to diversity.
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Citation: Das, Subhashis, and Fausto Giunchiglia. "Geoetypes: Harmonizing diversity in geospatial data (short paper)." In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems", pp. 643-653. Springer, Cham, 2016.