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Web-Scale Normalization of Geospatial Metadata Based on Semantics-Aware Data Sources

Institute for Electromagnetic Sensing of the Environment, National Research Council (IREA-CNR), 20133 Milan, Italy
Institute of Marine Science, National Research Council (ISMAR-CNR), 30122 Venice, Italy
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(11), 354;
Received: 29 September 2017 / Revised: 27 October 2017 / Accepted: 2 November 2017 / Published: 13 November 2017
Geospatial metadata are largely denormalized inasmuch as resource descriptions typically accommodate property values as plain text. Hence, it is not possible to bring multiple references to the same entity (say, a keyword from a controlled vocabulary) under the same umbrella. This practice is ultimately the main source for the heterogeneities in metadata descriptions by which geospatial discovery is hampered. In this paper, we elaborate on ex-post semantic augmentation of metadata, a technique generally referred to as semantic lift, which complements our previous research on semantic characterization of metadata via transparent association of uniform resource identifiers with metadata items at editing time. The latter is accomplished by means of a template-based metadata editor that can be tailored to any XML-based metadata schema. By repurposing the template language previously defined for metadata editing, we broaden the expressiveness of the former and integrate heterogeneous, XML-based resource descriptions in our semantics-aware metadata management workflow. URI-based indirection in metadata provision not only entails normalization of individual information items and allows one to overcome the aforementioned heterogeneities, but also elicits decentralized, multi-tenanted management of metadata. View Full-Text
Keywords: spatial data infrastructures; geospatial metadata; RDF; semantic lift spatial data infrastructures; geospatial metadata; RDF; semantic lift
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Fugazza, C.; Tagliolato, P.; Frigerio, L.; Carrara, P. Web-Scale Normalization of Geospatial Metadata Based on Semantics-Aware Data Sources. ISPRS Int. J. Geo-Inf. 2017, 6, 354.

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