# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## Data and Metadata Representation and Storage
### Geospatial Smart Representations  
The core of the smart data representations used by semantic technologies is the resource description framework (RDF) model [36]. Resources in RDF are identified with universal resource identifiers (URIs) and are described in terms of simple properties and values, which are also identified and represented with URIs. RDF expressions are triples of the form (subject, predicate, object), used to represent things (subject), properties of things (predicate) and values of those properties (object). Such values may also be identifiers of other resources, enabling this way the construction of complex knowledge graphs where things and values are nodes and properties are arcs. The use of standard text encodings of RDF graphs such as RDF XML and RDF Turtle eases the open interchange of RDF between systems. The vocabularies used in RDF statements (object and subject URIs) may be defined in RDF with RDF Schema primitives. Those primitives enable the definition of classes of objects using RDF Schema properties rdf:type (to state that an object is an instance of a specific class) and rdfs:subClassOf (to define hierarchies of classes). The domain and range class (or data type) of each property may also be defined in RDF Schema, which also enables the definition of hierarchies of properties and sub-properties. The Ontology Web Language (OWL) [37] extends RDF Schema with advanced class  
Figure 6: Main structures of the OGC observations and measurements (O&M) data model. relationships that include property restrictions expressed with universal and existential quantifiers and advanced property relationships such as inverseof. RDF data and metadata (ontologies) may be accessed directly through the web via HTTP and web services. However, to enable the declarative querying of RDF graphs in RDF stores, W3C has defined a set of specifications of languages and protocols called SPARQL Protocol and RDF Query Language (SPARQL) [19]. The GeoSPARQL [12] geographic extension of SPARQL provides an RDFS/OWL vocabulary that is based on the OGC SFA data model and it contains the following elements: (i) A class geo:Feature to represent OGC Features with geometry, (ii) a data type geo:wktLiteral to represent geometric literals encoded in OGC WKT, (iii) a class geo:Geometry to represent feature geometries, (iv) standard properties geo:hasGeometry and geo:hasDefaultGeometry to link each Feature to its geometric property values and (v) standard property geo:asWkT to link a geometry with its WKT encoding of geo:wktLiteral data type. Support for continuous raster coverages with semantic technologies has not still been developed and incorporated into relevant standards. Many semantic data storage technologies have been developed to support the recording of RDF graphs. More specifically, there are implementations that support GeoSPARQL RDF graphs [38]. Metadata related to semantic dataset schemes are naturally represented with OWL ontologies. Other dataset level metadata may be represented in RDF using the Data Catalog Vocabulary (DCAT) [39] and its geospatial extension GeoDCAT-AP. Specific ontologies have already been proposed by the W3C to represent data quality and data provenance metadata. The W3C Data Quality Vocabulary (DQV) [40] represents five different types of quality information of a dataset: (i) a quality annotation that gives feedback and quality certificates about the dataset, (ii) a data quality standard to which the dataset conforms, (iii) a data quality policy or agreement, (iv) a data quality measure that may be either qualitative or quantitative and (v) an entity involved in the dataset provenance and distribution. Data quality metrics may be given for different data quality dimensions (for example its availability). Provenance data describes the entities involved in the generation of a dataset, which may give insight about its quality and reliability that must be taken into account during data fusion and analytic processes.