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Open AccessArticle

Semantic Profiles for Easing SensorML Description: Review and Proposal

Institute for Electromagnetic Sensing of the Environment, National Research Council (IREA-CNR), v. Bassini 15, 20133 Milan, Italy
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(8), 340;
Received: 15 May 2019 / Revised: 5 July 2019 / Accepted: 25 July 2019 / Published: 31 July 2019
(This article belongs to the Special Issue Geospatial Metadata)
The adoption of Sensor Web Enablement (SWE) practices by sensor maintainers is hampered by the inherent complexity of the Sensor Model Language (SensorML), its high expressiveness, and the scarce availability of editing tools. To overcome these issues, the Earth Observation (EO) community often recurs to SensorML profiles narrowing the range of admitted metadata structures and value ranges. Unfortunately, profiles frequently fall short of providing usable editing tools and comprehensive validation criteria, particularly for the difficulty of checking value ranges in the multi-tenanted domain of the Web of Data. In this paper, we provide an updated review of current practices, techniques, and tools for editing SensorML in the perspective of profile support and introduce our solution for effective profile definition. Beside allowing for formalization of a broad range of constraints that concur in defining a metadata profile, our proposal closes the gap between profile definition and actual editing of the corresponding metadata by allowing for ex-ante validation of the metadata that is produced. On this basis, we suggest the notion of Semantic Web SensorML profiles, characterized by a new family of constraints involving Semantic Web sources. We also discuss implementation of SensorML profiles with our tool and pinpoint the benefits with respect to the existing ex-post validation facilities provided by schema definition languages. View Full-Text
Keywords: sensors; SensorML; profiles; editor; semantics sensors; SensorML; profiles; editor; semantics
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MDPI and ACS Style

Tagliolato, P.; Fugazza, C.; Oggioni, A.; Carrara, P. Semantic Profiles for Easing SensorML Description: Review and Proposal. ISPRS Int. J. Geo-Inf. 2019, 8, 340.

AMA Style

Tagliolato P, Fugazza C, Oggioni A, Carrara P. Semantic Profiles for Easing SensorML Description: Review and Proposal. ISPRS International Journal of Geo-Information. 2019; 8(8):340.

Chicago/Turabian Style

Tagliolato, Paolo; Fugazza, Cristiano; Oggioni, Alessandro; Carrara, Paola. 2019. "Semantic Profiles for Easing SensorML Description: Review and Proposal" ISPRS Int. J. Geo-Inf. 8, no. 8: 340.

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