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Linked Data with hybrid services in Agriculture

Translation of AgroDataCube

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Linked Data with hybrid services in Agriculture

  1. 1. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Linked Data with hybrid services in Agriculture Raul Palma1, Rob Knapen2 1Poznan Supercomputing and Networking Center 2Wageningen University & Research 113th OGC Technical Committee meeting Toulouse, 19th November 2019 1
  2. 2. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Linked data publication • LD is increasingly becoming a popular method for publishing data on the Web • Improves data accessibility by both humans and machines, e.g., for finding, reuse and integration • Enables to discover more useful data through the links (and inferencing), and to exploit data with semantic queries • Growing number of datasets in the LOD cloud  1,239 datasets with 16,147 links (as of March 2019) • Coverage of the LOD cloud  Large cross-domain datasets (dbpedia, freebase, etc.)  Variable domain coverage (e.g., Geography, Government, BioInformatics) • What about Agriculture?  “Just” few datasets (e.g., AGRIS biblio records, AGROVOC thesaurus + other thesaurus like NALT)  Farming data and other agri-activities related data? 2 http://lod-cloud.net/
  3. 3. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Why is Linked Data relevant in Agriculture: Farming context • Farm management • Multiple activities and stakeholders • Multiple applications, tools and devices • Multiple data sources, types and formats • Challenge  To combine/integrate those different and heterogeneous data sources in order to make economically and environmentally sound decisions 3
  4. 4. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Data Integration in relevant projects (context) • Data integration challenges have been/are one of the key challenges addressed in several recent projects related to the agri-food sector 4
  5. 5. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Linked data principles principles and general tasks • Simple set of principles & technologies • URI, HTTP, RDF, SPARQL • Involves a set of (common) general tasks 5 Datasets identification Model specification RDF data generation Linking Exploiting Hyland et al. Hausenblas et al. Villazón-Terrazas et al. Best Practices for Publishing Linked Data 5-star deployment scheme for Linked Open Data
  6. 6. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Linked data guidelines & patterns 6 T. Heath and C. Bizer. Linked Data: Evolving the Web into a Global Data Space, http://linkeddatabook.com/editions/1.0/ B. Hyland, G. Atemezing, B. Villazón-Terrazas Best Practices for Publishing Linked Data. W3C Working Group Note https://www.w3.org/TR/ld-bp/
  7. 7. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. From guidelines to practice 7
  8. 8. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Implementing Linked Data publication pipelines • Goal: to define and deploy (semi-) automatic processes to carry out the necessary steps to transform and publish different input datasets as Linked Data. • A pipeline connect different data processing components to carry out the transformation of data into RDF and their linking, and includes the mapping specifications to process the input datasets. • Each pipeline is configured to support specific input dataset types (same format, model and delivery form). • Principles  Pipelines can be directly re-executed and re-applied (e.g., extended/updated datasets)  Pipelines must be easily reusable  Pipelines must be easily adapted for new input datasets  Pipeline execution should be as automatic as possible. The final target is to fully automated processes.  Pipelines should support both: (mostly) static data and data streams (e.g., sensor data) • The resulting datasets available as Linked Data, will provide an integrated view over the initial (disconnected and heterogeneous) datasets, in compliance with any privacy and access control needs 8
  9. 9. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Serving Linked Data with hybrid services • Many practical linked data use cases have to address hybrid information needs1:  Variety of data sources  Variety of data modalities  Variety of data processing techniques • Although SPARQL queries enable to express data requests over RDF knowledge graphs, the support for hybrid information needs is limited  Query engines focus on retrieving RDF data and support a set of built-in services • Approach: implement wrappers around the APIs that:  Assign HTTP URIs to the resources about which the API provides data  Upon URI dereference, rewrite the client’s request into a request against the API  Transform API results to RDF and sent back to the client. 9 1Nikolov, Andriy et al. “Ephedra: SPARQL Federation over RDF Data and Services.” International Semantic Web Conference (2017).
  10. 10. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Use case: AgroDataCube (ongoing work) • AgroDataCube provides a large collection of both open and derived data from Netherlands for use in agri-food applications (by Wageningen Environmental Research) • AgroDataCube exposes a REST API with 6 resources:  Fields: to retrieve data from the crop registration datasets. Crop fields change per year, and are recorded by farmers with an indication of the crop that will be grown on the field.  Altitude: to retrieve AHN ('Actueel Hoogtebestand Nederland')  Meteo: to retrieve data from the KNMI (the Royal Netherlands Meteorological Institute) weather stations  Soil: to retrieve data from the BOFEK 2012 datasets and the Dutch soil map 1:50.000  Vegetation: to retrieve NDVI (Normalized Difference Vegetation Index) data  Codes: to retrieve more details about a specific crop or soil code returned by other requests  Regions: to retrieve administrative boundaries of provinces, municipalities, and postal code areas • Data is returned in GeoJSON format • Part of CYBELE demonstrator „Optimising computations for crop yield forecasting” 10
  11. 11. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. General steps • Define/select semantic models to represent data of resources from API • Implement wrapper around API to transform on the fly SPARQL request to API call and generate RDF data from GeoJSON result • Expose generated RDF data via SPARQL endpoint • Query REST API with SPARQL  Process (e.g., format) any required output on the fly  Link the generated RDF data with other datasets and thesauri (on the fly or with previously generated/discovered RDF links) • Visualize and exploit Linked Data 11
  12. 12. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Ontologies for AgroDataCube • General rule: reuse standard and/or widely used ontologies/vocabularies whenever possible, and extend as needed • Selected resources:  FOODIE ontology  OLU vocabulary  SOSA/SSN  Soilphysics  … 12
  13. 13. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. FOODIE ontology • Application vocabulary covering the different categories of information dealt by the farm mgmt. tools/apps • in line with existing standards and best practices  Builds on the INSPIRE AF specification for agricultural data, and  the INSPIRE specification for themes in annex I for geospatial data, based on  ISO/OGC standards for geographical information • Generated (semi-)automatically with ShapeChange tool from base model in UML1  ShapeChange implements ISO 19150-2 standard rules for mapping ISO geographic information UML models to OWL ontologies. • Overall structure (ShapeChange output)  UML featureTypes and dataTypes modelled as classes, and their attributes as datatype or object properties  UML codeLists modelled as classes/concepts, and their attributes as concept members  Cardinalities restrictions defined on properties (exactly, min, max)  DataType properties ranges defined according to model/mappings  Object properties ranges defined according to model/mappings  Object properties inverseOf defined 13 1Palma R., Reznik T., Esbri M., Charvat K., Mazurek C., An INSPIRE-based vocabulary for the publication of Agricultural Linked Data. Proceedings of the OWLED Workshop: collocated with the ISWC-2015, Bethlehem PA, USA, October 11-15, 2015 Datatype hierarchy codelist hierarchy FeatureType hierarchy
  14. 14. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. FOODIE ontology • Key feature on a more detailed level than Site that is already part of the INSPIRE AF data model: Plot • Represents a continuous area of agricultural land with one type of crop species, cultivated by one user in one farming mode • Two kinds of data associated: • metadata information • agro-related information  Next level: Management Zone • Enables a more precise description of the land characteristics in fine-grained area
  15. 15. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. FOODIE ontology • The Intervention is the basic feature type for any kind of (farming) application with explicitly defined geometry, e.g., tillage or pruning.  Has multiple indirect associations with different concepts 15
  16. 16. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Ephedra: API Wrapper • Ephedra is a SPARQL federation engine aimed at processing hybrid queries, which provides a flexible declarative mechanism for including hybrid services into a SPARQL federation. • Ephedra is a component of Metaphactory (https://www.metaphacts.com/), an end-to-end Knowledge Graph Platform for knowledge graph management, rapid application development, and end-user oriented interaction. 16
  17. 17. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Creating SPARQL wrapper with Ephedra • Describe the REST Service Signature (mapping) 17
  18. 18. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Creating SPARQL wrapper with Ephedra • Configure the AgroDataCube REST Service Repository • Include this repository into the Ephedra federation 18
  19. 19. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Expose generated RDF data via SPARQL endpoint • SPARQL endpoint provided  http://metaphactory.foodie- cloud.org/sparql?repository=ephedra • Use SPARQL SERVICE keyword  SERVICE <http://www.metaphacts.com/ontologies/platform/rep ository/federation#agrodatacube> 19
  20. 20. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Query REST API with SPARQL • Process (e.g., format) any required output on the fly • Link the generated RDF data with other datasets and thesauri on the fly 20
  21. 21. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Visualize and exploit the linked data • Demo app: http://metaphactory.foodie-cloud.org/resource/:AGROVOC-crops 21
  22. 22. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Visualize and exploit the linked data • Demo app: http://metaphactory.foodie-cloud.org/resource/:AGROVOC-crops 22
  23. 23. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Visualize and exploit the linked data • Demo app: http://metaphactory.foodie-cloud.org/resource/:AGROVOC-crops 23
  24. 24. www.cybele-project.eu This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355. Special thanks to Metaphacts team Questions: rpalma@man.poznan.pl 24 Thank you!

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