Motivation ---------- Spatial Data Infrastructures (SDI) developed for the exchange of environmental has heretofore been greatly shaped by the standards issued by the Open Geospatial Consortium (OGC). Based on the Simple Object Access Protocol (SOAP), services like WMS, WFS, WCS, CSW became digital staples for researchers and administrative bodies alike. In 2017 the Spatial Data on the Web Working Group (SDWWG) questioned the overall approach of the OGC, based on the ageing SOAP technology [@SDWWG2017]. The main issues identified by the SDWWG can be summarised as: - Spatial resources are not identified with URIs. - Modern API frameworks, e.g. OpenAPI, are not being used. - Spatial data are still shared in silos, without links to other resources. - Content indexing by search engines is not facilitated. - Catalogue services only provide access to metadata, not the data. - Data difficult to understand by non-domain-experts. To address these issues the SDWWG proposed a five point strategy inspired on the Five Star Scheme [@BernersLee2006]: - *Linkable*: use stable and discoverable global identifiers. - *Parseable*: use standardised data meta-models such as CSV, XML, RDF, or JSON. - *Understandable*: use well-known, well-documented, vocabularies/schemas. - *Linked*: link to other resources whenever possible. - *Usable*: label data resources with a licence. The work of the SDWWG triggered a transformational shift at the OGC towards specifications based on the OpenAPI. But while convenience of use has been the focus, semantics has been largely unheeded. A Linked Data agenda has not been pursued. However, the OpenAPI opens the door to an informal coupling of OGC services with the Semantic Web, considering the possibility of adopting JSON-LD as syntax to OGC API responses. The introduction of a semantic layer to digital environmental data shared through state-of-the-art OGC APIs is becoming a reality, with great benefits to researchers using or sharing data. This communication lays down a simple SDI set up to serve semantic environmental data through a SensorThings API created with the `glrc` software. A use case is presented with soil data services compliant with the GloSIS web ontology. SensorThings API ---------------- SensorThings API is an OGC standard specifying a unified framework to interconnect Internet of Things resources over the Web [@liang2016ogc]. SensorThings API aims to address both the semantic, as well as syntactic, interoperability. It follows ReST principles [@fielding2002principled], promotes data encoding with JSON, the OASIS OData protocol [@chappell2011introducing] and URL conventions. The SensorThings API is underpinned on a domain model aligned with the ISO/OGC standard Observations & Measurements (O&M) [@Cox2011], targeted at the interchange of observation data of natural phenomena. O&M puts forth the concept of `Observation` has an action performed on a `Feature of Interest` with the goal of measuring a certain `Property` through a specific `Procedure`. SensorThings API mirrors these concepts with `Observation`, `Thing`, `ObservedProperty` and `Sensor`. This character makes of SensorThings API a vehicle for the interoperability of heterogeneous sources of environmental data. `glrc` ------ `grlc` (pronounced "garlic") is a lightweight server that translates SPARQL queries into Linked Data web APIs [@merono2016grlc] compliant with the OpenAPI specification. Its purpose is to enable universal access to Linked Data sources through modern web-based mechanisms, dispensing the use of the SPARQL query language. While losing the flexibility and federative capacities of SPARQL, web APIs present developers with an approachable interface that can be used for the automatic generation of source code. A `glrc` API is constructed from a SPARQL query to which a meta-data section is prepended. This section is declared with a simplified YAML syntax, within a SPARQL comment block, so the query remains valid SPARQL. The meta-data provide basic information for the API set up and most importantly, the SPARQL end-point on which to apply the query. The listing below shows an example. ` #+ endpoint: http://dbpedia.org/sparql PREFIX dbo: <http://dbpedia.org/ontology/> PREFIX dbr: <http://dbpedia.org/resource/> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT ?band_label { ?band rdf:type dbo:Band ; dbo:genre dbr:Hard_Rock ; rdfs:label ?band_label . } ORDER BY ?band_label ` A special SPARQL variable formulation is used to map into API parameters. By adding an underscore (_) between the question mark and the variable name, glrc is instructed to create a new API parameter. A prefix separated again with an underscore informs glrc of the parameter type. The ?band_label variable can be expanded to ?_band_label_iri to create a new API parameter of the type IRI.