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Streamlining INSPIRE metadata for the Semantic Web

Cristiano Fugazza, Monica Pepe, Alessandro Oggioni, Paolo Tagliolato and Paola Carrara

(Submission #415)


Metadata are at the heart of the discovery process of geospatial datasets and nevertheless they are frequently neglected. When not overlooked, they are often encoded in formats that either are not aimed at efficient retrieval of resources or are plainly outdated. Particularly, the quantum leap represented by the Linked Open Data (LOD) movement did not induce so far a consistent, interlinked baseline in the geospatial domain. In a nutshell, datasets, scientific literature related to them, and ultimately the researchers behind these products are only loosely connected; the corresponding metadata intelligible only to humans, duplicated on different systems, seldom consistently.

Addressing these issues, our methodology for metadata management envisages i) editing via customisable web-based forms, ii) encoding of records in any XML metadata schema, iii) translation into RDF (involving the semantic lift of metadata records), and finally iv) storage of the metadata as RDF and back-translation into the original XML format with added semantics-aware features. Phase iii) hinges on relating resource metadata to RDF data structures that represent keywords from code lists and controlled vocabularies, toponyms, researchers, institutes, and virtually any description one can retrieve (or directly publish) in the LOD Cloud.

The net product is a framework fostering delegated, evolving metadata management as the entities referred to in metadata, and the property values they bear with them, are independent, decentralised data structures with their own life cycle. We present a use case demonstrating the capabilities of our approach in the context of INSPIRE metadata. Still, the tools and techniques we present are also applicable to the broad range of ISO 19115/19119 profiles as well as to generic XML metadata schemas.


Topic Area:  [2.2] Technologies and tools required to deliver INSPIRE
Abstract Type:  Oral Presentation

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