# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 5 Data Searching and Browsing
### Smart Data Searching and Browsing  
Smart data searching and browsing solutions should leverage semantic geospatial and environmental data representations and combine them with existing semantic technologies to produce a better user experience. Some research approaches have already tried to achieve that by incorporating semantic technologies in OGC web services. As an example, RDF is used in [103] to improve data discovery in geographic data portals. Data discovery through CSW services is also improved in [104] with the use of ontologies. In [72], SensorML descriptions of sensor systems are semantically annotated to enable their registration in SOS services. A semantic SOS (SemSOS) implementation is described in [105]. Semantically annotated O&M and SensorML documents are generated from semantically annotated observation data and metadata using SPARQL. Finally, the SOS-based semantic mediation approach described in [45] provides also enhanced searching supported by basic reasoning over integrated global and local ontologies. A different approach is the incorporation of geospatial data management capabilities in semantic web data management technologies. As it was already mentioned in Section 3, declarative querying of RDF datasets in the semantic web is supported with SPARQL [19]. This is a set of standard specifications that include a query language, an update language, a protocol to submit queries and update request to SPARQL services (SPARQL end points), a method for discovering and a vocabulary for describing SPARQL services, definitions of JSON, XML, CSV and TSV encodings for query results, and extension for federated queries and the definition of semantics of SPARQL queries under different entailment regimes. SPARQL has already been extended to support geospatial queries over vector feature datasets. Such an extension is called GeoSPARQL [12]. The RDF/OWL vocabulary that enables the representation of vector features in GeoSPARQL was already briefly described in Section 3. Beyond gespatial data representation, GeoSPARQL includes also primitive geospatial predicates and functions, based on OGC SFA [26], to support geospatial query formulations. Implementation of GeoSPARQL are also available [38]. Raster coverages are not supported in GeoSPARQL, as they are also not supported in standard SQL. To the best of these authors' knowledge, only the SciSPARQL solution [53] has attempted to incorporate raster coverage management in SPARQL. However, this approach suffers from similar problems to those already reported above for spatial databases with raster data types, i.e., high complexity in query specification due to the nested model and low performance. Discovering semantic datasets is achieved by querying metadata catalogs, that may be represented using the DCAT standard vocabulary [39]. An application profile of DCAT, called DCAT-AP has been defined for European Data Portals, and an extension of DCAT-AP, called GeoDCAT-AP incorporates specific metadata of geospatial datasets. Based on the above review of currently available technologies and approaches for semantic searching and browsing over geospatial data, the following challenges are identified that should be undertaken to achieve really effective and efficient smart geospatial searching and browsing (see gray boxes in Figure 11). **Challenge 7: semantic federated querying over vector and raster datasets. **: Most of the available geospatial datasets are not stored with semantic technologies, on the other hand, as it is illustrated at the bottom of Figure 11, to construct integrated datasets, ETL task may need to access semantic stores, generally to retrieve metadata, spatial databases or files to retrieve vector features and array files or managers to retrieve raster coverages. The above technologies are heterogeneous not only in their storage formats, but also in their query capabilities. The definition of model mapping solutions from geospatial vector and raster models to RDF have already been identified as a challenge (challenge 4) in Section 4.