In computing, linked data (often capitalized as Linked Data) is a method of publishing structured data so that it can be interlinked and become more useful through semantic queries. It builds upon standard Web technologies such as HTTP, RDF and URLs, but rather than using them to serve web pages for human readers, it extends them to share information in a way that can be read automatically by computers.

Tim Berners-Lee, director of the World Wide Web Consortium (W3C), coined the term in a 2006 design note about the Semantic Web project.

Linked data may also be open data, in which case it is usually described as linked open data (LOD).

The goal of the W3C Semantic Web Education and Outreach group’s Linking Open Data community project is to extend the Web with a data commons by publishing various open datasets as RDF on the Web and by setting RDF links between data items from different data sources. In October 2007, datasets consisted of over two billion RDF triples, which were interlinked by over two million RDF links. By September 2011 this had grown to 31 billion RDF triples, interlinked by around 504 million RDF links. A detailed statistical breakdown was published in 2014.

 


LEAF AREA INDEX

  • Description

    This global database of Leaf Area Indices (LAIs) is derived using input from the Moderate Resolution Imaging Spectroradiometer (MODIS) operational reflectance product (MOD09). The LAI datasets were created by reprocessing the MODIS LAI products using a two-step integrated method...

    First, a modified temporal spatial filter (mTSF) was used based on the TSF method developed by Fang et al. (2007; 2008). This to fill the gaps of the MODIS LAI data and process the lower quality data according to the quality control (QC) value information by making the best use of the high quality data. Then the TIMESAT Savitzky-Golay (SG) filter (Jonsson and Eklundh, 2004) was applied to generate the improved MODIS LAI products.

     

  • Ontology

    The following is a simple ontology for the LAI dataset. We took into consideration sensor/observation ontologies such as SSN https://www.w3.org/TR/vocab-ssn/ and the ontologies proposed by the “Coverages in Linked Data” activity of the W3C/OGC Working Group on Spatial Data on the Web (https://www.w3.org/TR/eo-qb/) ...

    But since they are generic ontologies, we created are own simple ontology for the LAI dataset and then we aligned it with the SOSA (Sensor, Observation, Sample, and Actuator) Ontology (https://www.w3.org/ns/sosa/). Our Observation class is equivalent to the sosa: Observation class, while the properties lai and observationTime of our ontology are sub-properties of the more general hasSimpleResult and resultTime properties of the SOSA ontology.

  • Sample Data

    The RDF graph described here represents information about the Leaf Area Index (LAI) of areas, the locations of these areas, as well as the respective observation times...

    PREFIX lai:<https://ramani.ujuizi.com/thredds/dodsC/Sentinel-3-timeseries-global-lai/> PREFIX rdf: <http://www.w3.org/TR/rdf-schema/> PREFIX geo: <http://www.opengis.net/ont/geosparql#> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> lai:884_266_0 rdf:type lai:Observation . lai:884_266_0 geo:hasGeometry geo:884_266 . geo:884_266 geo:asWKT “POINT(2.22917617066 48.8624978545)”^^geo:wktLiteral . lai:884_266_0 lai:observationTime “2003-06-01T00:00:00″^^xsd:dateTime . lai:884_266_0 lai:lai “1.2”^^xsd:float .

  • Sample Queries

    Example query: Retrieve how many Corine areas belong to every land use category and project the union of the geometries of these areas per category...

    PREFIX lai:<http://geo.linkedopendata.gr/lai/ontology/> PREFIX corine:<http://geo.linkedopendata.gr/corine/ontology#> PREFIX gadm:<http://geo.linkedopendata.gr/gadm/ontology#> PREFIX geo:<http://www.opengis.net/ont/geosparql#> PREFIX rdf:<http://www.w3.org/TR/rdf-schema/> PREFIX geof: <http://www.opengis.net/def/function/geosparql/> PREFIX strdf: <http://strdf.di.uoa.gr/ontology#> SELECT distinct ?l (strdf:union(?w3) as ?geo) (count(?c) as ?instances) WHERE { ?c rdf:type clc:Area . ?c corine:hasLandUse ?l . ?c geo:hasGeometry ?geo3 . ?geo3 geo:asWKT ?w3 . } GROUP BY ?l

  • Endpoint
  • Access Methods

    You can consume Linked Data using HTTP requests. To do that you will pose a GET request with a query parameter query...

    SPARQL query string (url encoded).

To get the results in specific formats you can use Accept header according to the required results format:
 1. application/sparql-results+xml (XML)
 2. application/sparql-results+json (JSON)
 3. text/tab-separated-values (TSV)
 4. text/html (HTML table)
 5. application/json OR application/geojson (GeoJSON)
 6. application/kml (KML) For more information you can read the SPARQL 1.1 Protocol https://www.w3.org/TR/sparql11-protocol/


     

CORINE LAND COVER 2012

  • Description

    The Corine Land Cover dataset of year 2012 (CLC2012) is provided by the European Environment Agency (EEA) and it can be accessed at the following link http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012

    The CORINE Land Cover (CLC) inventory was initiated in 1985 (reference year 1990). Updates have been produced in 2000, 2006, and 2012. It consists of an inventory of land cover in 44 classes. CLC uses a Minimum Mapping Unit (MMU) of 25 hectares (ha) for areal phenomena and a minimum width of 100 m for linear phenomena. The time series are complemented by change layers, which highlight changes in land cover with an MMU of 5 ha. Different MMUs mean that the change layer has higher resolution than the status layer. Due to differences in MMUs the difference between two status layers will not equal to the corresponding CLC-Changes layer. If you are interested in CLC-Changes between two neighbour surveys always use the CLC-Change layer.

  • Ontology

    The figure, which is included in the following link http://pyravlos-vm5.di.uoa.gr/corineLandCover.svg, shows the ontology constructed for the CLC dataset...

    The ontology is a specialization of the general ontology that we constructed to model the respective Land Cover theme of INSPIRE so that we have the first INSPIRE-compliant ontology, which can be found in the following link http://pyravlos-vm5.di.uoa.gr/LandCover.svg. The following RDF entities are being used, for the Corine Land Cover ontology: CorineValue: This entity represents Corine nomenclature, as a codelist. It includes recommended values that may be used by data providers. CorineArea: This entity is defined as a subclass of the LandCoverUnit entity, of the INSPIRE theme Land Cover. The LandCoverUnit represents a section of space which is classified, and corresponds to a Corine polygon. objectId: The entity that indicates the unique id of a Corine Land Cover area. It is a literal of type xsd:ID. codeLevel1, codeLevel2, codeLevel3: These three entities represent the Corine Land Cover code of this area for the three different levels respectively. It is a literal of type xsd:integer. Geometry: The entity that represents the geometry of an area. It is a POLYGON geometry literal of type wktLiteral.

  • Sample Data

    The first triple denotes that area clc: Area_361450 is a Corine Land Cover area. The next three triples represent the Corine Land Cover code of this area for the three different levels respectively...

    It is noted that given the third level code, one can derive the codes for the other two levels, but we chose to state this explicitly in the dataset so that it is more user-friendly (less calculations need to be done by the users). After that, we include a triple that explicitly states the name of the land use class that the CLC code corresponds to. This again was not explicit in the original dataset but it would be helpful for someone that is not familiar with the CLC nomenclature so we decided to minimize as much as possible amount of background knowledge one may need to have in order to use the dataset. The last two triples express the geometry of the specific area. Please note that the predicates geo:hasGeometry and geo:asWKT are defined in GeoSPARQL (that is described earlier in this document) and they are used to associate a CLC area with its geometry and then this geometry to its WKT serialization. This serialization is represented by a literal with datatype geo:wktLiteral, following again the OGC GeoSPARQL standard. For the convenience of the reader and for clarity, we have not included all points that this specific geometry (polygon) consists of. clc:Area_361450 rdf:type clc:Area . clc:Area_361450 clc:codeLevel1 “3”^^xsd:integer . clc:Area_361450 clc:codeLevel2 “31”^^xsd:integer . clc:Area_361450 clc:codeLevel3 “312”^^xsd:integer . clc:Area_361450 clc:hasLandUse “complexCultivation” . clc:Area_361450 geo:hasGeometry clc:Geometry_361450 . clc:Geometry_361450 geo:asWKT “&lt;http://www.opengis.net/def/crs/EPSG/0/4326&gt; POLYGON((1.149187 48.456576, 1.148363 48.455932, 1.146745 48.455954, 1.145644 48.4557, 1.145648 48.454781, 1.145444 48.454345, 1.143585 48.454364, 1.142694 48.453517,…, 1.149187 48.456576))”^^&lt; http://www.opengis.net/ont/geosparql#wktLiteral&gt;

  • Sample Queries

    Example query: Retrieve how many Corine areas belong to every land use category and project the union of the geometries of these areas per category...

    PREFIX lai:&lt;http://geo.linkedopendata.gr/lai/ontology/&gt; PREFIX corine:&lt;http://geo.linkedopendata.gr/corine/ontology#&gt; PREFIX gadm:&lt;http://geo.linkedopendata.gr/gadm/ontology#&gt; PREFIX geo:&lt;http://www.opengis.net/ont/geosparql#&gt; PREFIX rdf:&lt;http://www.w3.org/TR/rdf-schema/&gt; PREFIX geof: &lt;http://www.opengis.net/def/function/geosparql/&gt; PREFIX strdf: &lt;http://strdf.di.uoa.gr/ontology#&gt; SELECT distinct ?l (strdf:union(?w3) as ?geo) (count(?c) as ?instances) WHERE { ?c rdf:type clc:Area . ?c corine:hasLandUse ?l . ?c geo:hasGeometry ?geo3 . ?geo3 geo:asWKT ?w3 . } GROUP BY ?l

  • Endpoint
  • Access Methods

    You can consume Linked Data using HTTP requests. To do that you will pose a GET request with a query parameter query...

    SPARQL query string (url encoded).

    To get the results in specific formats you can use Accept header according to the required results format:
    1. application/sparql-results+xml (XML)
    2. application/sparql-results+json (JSON)
    3. text/tab-separated-values (TSV)
    4. text/html (HTML table)
    5. application/json OR application/geojson (GeoJSON)
    6. application/kml (KML) For more information you can read the SPARQL 1.1 Protocol https://www.w3.org/TR/sparql11-protocol/

GLOBAL ADMINISTRATIVE AREAS

  • Description

    The Global Administrative Areas dataset (GADM) contains information about the administrative boundaries of all areas in the world...

    These datasets are available as shapefiles or geodatabase files at the following link: http://www.gadm.org. Although this is not Copernicus data, it can be very useful when combined with Copernicus data, for example, one may ask for information contained in a Copernicus dataset, but only for a specific country, or an administrative division of these countries. This also enables users to express analytical queries for certain administrative divisions.

  • Ontology

    The following RDF entities are being used, for the GADM ontology: AdministrativeArea: This entity is defined as an equivalent class of the AdministrativeUnit entity, of the INSPIRE theme Administrative Units...

    The AdministrativeUnit entity represents a unit of administration, where a Member State has and/or exercises jurisdictional rights, for local, regional and national governance. belongsToAdm0, belongsToAdm1, belongsToAdm2: These three entities represent the global administrative boundaries, for three levels: National (level 0), State/province/equivalent (level 1), and County/district/equivalent (level 2). hasName: The entity that indicates the name of the Administrative Area. It is a literal of type xsd:string.

  • Sample Data

    The RDF graph described here contains information about and administrative unit identified with the URI gadm:adm3_1, its name, its administrative levels, information about the administrative levels it belongs to, as well as the geometries of its boundaries in WKT format. clc:Area_361450 rdf:type clc:Area...

    clc:Area_361450 clc:codeLevel1 “3”^^xsd:integer . clc:Area_361450 clc:codeLevel2 “31”^^xsd:integer . clc:Area_361450 clc:codeLevel3 “312”^^xsd:integer . clc:Area_361450 clc:hasLandUse “complexCultivation” . clc:Area_361450 geo:hasGeometry clc:Geometry_361450 . clc:Geometry_361450 geo:asWKT “<http://www.opengis.net/def/crs/EPSG/0/4326> POLYGON((1.149187 48.456576, 1.148363 48.455932, 1.146745 48.455954, 1.145644 48.4557, 1.145648 48.454781, 1.145444 48.454345, 1.143585 48.454364, 1.142694 48.453517,…, 1.149187 48.456576))”^^<http://www.opengis.net/ont/geosparql#wktLiteral>

  • Sample Queries

    Example query: Retrieve how many Corine areas belong to every land use category and project the union of the geometries of these areas per category...

    PREFIX lai:<http://geo.linkedopendata.gr/lai/ontology/> PREFIX corine:<http://geo.linkedopendata.gr/corine/ontology#> PREFIX gadm:<http://geo.linkedopendata.gr/gadm/ontology#> PREFIX geo:<http://www.opengis.net/ont/geosparql#> PREFIX rdf:<http://www.w3.org/TR/rdf-schema/> PREFIX geof: <http://www.opengis.net/def/function/geosparql/> PREFIX strdf: <http://strdf.di.uoa.gr/ontology#> SELECT distinct ?l (strdf:union(?w3) as ?geo) (count(?c) as ?instances) WHERE { ?c rdf:type clc:Area . ?c corine:hasLandUse ?l . ?c geo:hasGeometry ?geo3 . ?geo3 geo:asWKT ?w3 . } GROUP BY ?l

  • Endpoint
  • Access Methods

    You can consume Linked Data using HTTP requests. To do that you will pose a GET request with a query parameter query...

    SPARQL query string (url encoded).

To get the results in specific formats you can use Accept header according to the required results format:
 1. application/sparql-results+xml (XML)
 2. application/sparql-results+json (JSON)
 3. text/tab-separated-values (TSV)
 4. text/html (HTML table)
 5. application/json OR application/geojson (GeoJSON)
 6. application/kml (KML) For more information you can read the SPARQL 1.1 Protocol https://www.w3.org/TR/sparql11-protocol/

EU-HYDRO RIVER NETWORK

OZONE FORECAST

  • Description

    This dataset is provided by the Atmosphere Copernicus Service. We acquired it through the RAMANI Opendap interface and have converted it into RDF...

    The dataset provides information for air quality, specifically observations for Nitrogen Dioxide (NO2), Ozone (O3) and UV emissions.

  • Ontology

    In an effort to efficiently map the netCDF's data into RDF, the following RDF entities are being used for the time series ontology...

    Area: an area of the dataset’s grid. Nitrogen_dioxide: the entity that represents the nitrogen dioxide (NO2) emission. It is a literal of type float (measured in kg x kg-1). GEMS_Ozone: the entity that represents the GEMS ozone (O3) emission. It is a literal of type float (measured in kg x kg-1). UV_biologically_effective_dose: the entity that represents the UV emission. It is a literal of type float (measured in kg x kg-1). ObservationTime: This property that represents the observation time of the previous values at a specific step and location. Its values are literals of type xsd:dateTime. Geometry: the entity that represents the geometry of each point of the grid. It is a POINT geometry literal of type wktLiteral.

  • Sample Data

    The triples here represent the observation time (2016-07-12T00:00:00) of the emission of NO2 (8.67753060977-09 kg x kg-1), O3 (5.5209340308-08 kg x kg-1) and UV (0.0 kg x kg-1) at the 53rd latitude and 49th longitude of the grid of the dataset, which is a geometry of type point (wktLiteral, POINT(-3.75 51.0))...

    It is important to note that the coordinates of the geometries are in the format POINT(longitude latitude). Also it is important to notice the kg x kg-1 units of the values. While it may seem that kg and kg-1 cancel out each other, the measurement is kilogram of water (moisture) per kilogram of air and that is used to avoid confusion with vol.%. PREFIX air: <http://ramani.ujuizi.com/thredds/dodsC/Sentinel-5-timeseries-global-air/> PREFIX geo: <http://www.opengis.net/ont/geosparql> PREFIX area: <http://www.w3.org/TR/rdf-schema/type> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> air:Area_1_53_53 geo:hasGeometry air:Geometry_53_53 . air:Area_1_53_53 air:observationTime “2016-07-12T00:00:00″^^xsd:dateTime . air:Area_1_53_53 air:Nitrogen_dioxide “8.09296709564e-09″^^xsd:float . air:Area_1_53_53 air:GEMS_Ozone “6.31576384049e-08″^^xsd:float . air:Area_1_53_53 air:ontology/UV_biologically_effective_dose “0.0”^^xsd:float . air:Area_1_53_53 rdf:type air:Area .

  • Sample Queries

    Example query: Retrieve the areas that have average UV more than 2.36 and project also the respective average Ozone values...

    PREFIX air: <http://ramani.ujuizi.com/thredds/dodsC/Sentinel-5-timeseries-global-air/> PREFIX geo: <http://www.opengis.net/ont/geosparql#> SELECT (avg(?uv) as ?avguv) (avg(?o) as ?avgo) ?g WHERE { ?s air:GEMS_Ozone ?o . ?s air:UV_biologically_effective_dose ?uv . ?s geo:hasGeometry ?g1 . ?g1 geo:asWKT ?g . ?s air:observationTime ?t. } GROUP BY ?g HAVING (?avguv > 2.36) ORDER BY desc(?avguv)

  • Endpoint
  • Access Methods

    You can consume Linked Data using HTTP requests. To do that you will pose a GET request with a query parameter query...

    SPARQL query string (url encoded).

To get the results in specific formats you can use Accept header according to the required results format:
 1. application/sparql-results+xml (XML)
 2. application/sparql-results+json (JSON)
 3. text/tab-separated-values (TSV)
 4. text/html (HTML table)
 5. application/json OR application/geojson (GeoJSON)
 6. application/kml (KML) For more information you can read the SPARQL 1.1 Protocol https://www.w3.org/TR/sparql11-protocol/

OPEN STREET MAP