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An INSPIRE-based vocabulary for the publication of Agricultural Linked Data

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An INSPIRE-based vocabulary for the publication of Agricultural Linked Data

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FOODIE project aims at building an open and interoperable agricultural specialized platform on the cloud for the management, discovery and large-scale integration of data relevant for farming production. In particular, the integration focuses on existing open datasets as well as their publication in Linked data format in order to maximize their reusability and enable the exploitation of the extra knowledge derived from the generated links. Based on such data, for instance, FOODIE platform aims at providing high-value applications and services supporting the planning and decision-making processes of different stakeholders related to the agricultural domain. The keystone for data integration is FOODIE data model, which has been defined by reusing and extending current standards and best practices, including data specifications from the INSPIRE directive which are in turn based on the ISO/OGC standards for geographical information. However, as these data specifications are available as XML documents, the first step to publish Linked Data required transforming or lifting FOODIE data model into semantic format. In this paper, we describe this process, which was conducted semi-automatically by reusing existing tools, and adhering to the mapping rules for transforming geographic information UML models to OWL ontologies defined by the ISO 19150-2 standard. We describe the challenges associated to this transformation, and finally, we describe the generated ontology, providing an INSPIRE-based vocabulary for the publication of Agricultural Linked Data.

FOODIE project aims at building an open and interoperable agricultural specialized platform on the cloud for the management, discovery and large-scale integration of data relevant for farming production. In particular, the integration focuses on existing open datasets as well as their publication in Linked data format in order to maximize their reusability and enable the exploitation of the extra knowledge derived from the generated links. Based on such data, for instance, FOODIE platform aims at providing high-value applications and services supporting the planning and decision-making processes of different stakeholders related to the agricultural domain. The keystone for data integration is FOODIE data model, which has been defined by reusing and extending current standards and best practices, including data specifications from the INSPIRE directive which are in turn based on the ISO/OGC standards for geographical information. However, as these data specifications are available as XML documents, the first step to publish Linked Data required transforming or lifting FOODIE data model into semantic format. In this paper, we describe this process, which was conducted semi-automatically by reusing existing tools, and adhering to the mapping rules for transforming geographic information UML models to OWL ontologies defined by the ISO 19150-2 standard. We describe the challenges associated to this transformation, and finally, we describe the generated ontology, providing an INSPIRE-based vocabulary for the publication of Agricultural Linked Data.

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An INSPIRE-based vocabulary for the publication of Agricultural Linked Data

  1. 1. An INSPIRE-based vocabulary for the publication of Agricultural Linked Data OWLED Workshop 2015 Bethlehem, PA. USA 9th October 2015 Raul Palma, Tomas Reznik, Miguel Esbri, Karel Charvat, and Cezary Mazurek w w w . f o o d i e - p r o j e c t . e u Grant agreement no: 621074 CIP-ICT-PSP-2013-7 Pilot Type B
  2. 2. 2www.foodie-project.eu Presentation Outline 1. Motivation and context 2. Transformation Process 3. Ontology 4. Discussion and Future Work
  3. 3. 3www.foodie-project.eu Fig1 - GVA Fig2 - Employment Fig3 – Land use  Agriculture sector has high importance in the EU economy • The agriculture+ sector represented about 1,7% of the EU-28 Gross Value Added(fig1) and accounted for 4.9% of the total number of persons employed in 2013 (fig2) • Around 40 % of the total EU-28 land area is used for farming and for forestry (fig3)  Accordingly, EU has developed policies and innovation programs that tackle • the challenges associated to improve the efficiency of agricultural activities • with a limited environmental footprint First some statistical facts Source: Eurostat
  4. 4. 4www.foodie-project.eu Agriculture sector overview • Multiple activities and stakeholders • Multiple applications, tools and devices • Multiple data sources, data types and data formats In order to make economically and environmentally sound decisions, the different stakeholders groups involved in the agricultural activities need integrated access to multiple and heterogeneous sources of information collected by multiple applications and devices. Complex ecosystem!
  5. 5. 5www.foodie-project.eu  FOODIE project aims at building an open and interoperable cloud-based platform addressing among others the integration of data relevant to farming production including their geo-spatial dimension, as well as their publication as Linked data.  Data integration (and system interoperability) at the semantic level involves • matching, aligning or translating models/schemas to provide a unified data view/access • linking data/model elements to a reference vocabulary, e.g., AGROVOC, other well-known  Publication of linked data involves • dataset identification, model specification, RDF data generation and linking • model provides an application vocabulary for representing cross-domain data and information specific to FOODIE FOODIE vision and goals (semantics) High-value apps & services supporting planning and decision-making
  6. 6. 6www.foodie-project.eu  Task1: (i) to define the application vocabulary covering the different categories of information the platform has to deal with, (ii) in line with existing standards and best practices  INSPIRE directive is an EU initiative that aims at building a Pan- European spatial data infrastructure (SDI) requiring • EU Member States to make available spatial data, from multiple thematic areas, according to established implementing rules using appropriate services. • Based on ISO/OGC standards for geographical information, i.e., ISO 19100 series standards  Hence, FOODIE data model builds on • the INSPIRE data specification for agricultural and aquaculture facilities theme - AF (for agricultural data), and • the INSPIRE data specification for themes in annex I for for geospatial data FOODIE data model
  7. 7. 7www.foodie-project.eu  INSPIRE data specifications are defined as UML models and are available in different XML-based formats  FOODIE extensions on the UML model developed by domain experts  Task2: Transform model into OWL ontology FOODIE data model class Foodie Core Data Model v4.1 proposal «featureType» Agricultural and Aquaculture Facilities Model::Holding «featureType» Agricultural and Aquaculture Facilities Model:: Site + code :Identifier + geometry :GM_Object + activity :EconomicActivityNACEValue [1..*] + validFrom :DateTime + validTo :DateTime [0..1] + beginLifespanVersion :DateTime + endLifeSpanVersion :DateTime [0..1] «voidable» + includesAnimal :FarmAnimalSpecies [0..*] «dataType» Agricultural and Aquaculture Facilities Model:: FarmAnimalSpecies «voidable» + livestock :LivestockSpeciesValue [0..*] + aquaculture :AquacultureSpeciesValue [0..*] «featureType» Activity Complex::ActivityComplex + inspireId :Identifier + thematicId :ThematicIdentifier [0..*] + geometry :GM_Object + function :Function [1..*] + userId :CharacterString «voidable» + name :CharacterString [0..1] + validFrom :DateTime + validTo :DateTime [0..1] «voidable, lifeCycleInfo» + beginLifespanVersion :DateTime + endLifespanVersion :DateTime [0..1] «featureType» Plot + code :Identifier + validFrom :DateTime + validTo :DateTime [0..1] + beginLifeSpanVersion :DateTime + endLifeSpanVersion :DateTime [0..1] + geometry :GM_Object [1..*] + description :CharacterString [0..1] + originType :OriginTypeValue «featureType» Treatment + quantity :Measure [1..*] + tractorId :CharacterString [0..*] + machineId :CharacterString [0..*] + motionSpeed :Measure [0..1] + pressure :Measure [0..1] + flowAdjustment :Measure [0..1] + applicationWidth :Measure [0..1] + areaDose :DoseUnit [0..1] + formOfTreatment :FormOfTreatmentValue [1..*] + treatmentPurpose :TreatmentPurposeValue [0..*] + treatmentDescription :CharacterString [0..1] «dataType» NutrientsType + N :Measure [0..1] + P2O5 :Measure [0..1] + K2O :Measure [0..1] + MgO :Measure [0..1] + CaO :Measure [0..1] + S :Measure [0..1] + Zn :Measure [0..1] + Cu :Measure [0..1] + Fe :Measure [0..1] + B :Measure [0..1] + Mn :Measure [0..1] + Mo :Measure [0..1] «codeList» TreatmentPurposeValue + weed + pest + disease «featureType» CropSpecies + beginDate :Date + endDate :Date [0..1] + cropArea :GM_Object + cropSpecies :CropType [1..*] + production :ProductionType [0..*] «featureType» Product + productCode :CharacterString [0..*] + productName :CharacterString [1..*] + productType :CharacterString + productSubType :CharacterString [0..*] + productKind :ProductKindValue + description :CharacterString [0..1] + manufacturer :CI_ResponsibleParty [1..*] + nutrients :NutrientsType [0..*] + safetyInstructions :CharacterString [0..1] + storageHandling :CharacterString [0..1] + registrationCode :CharacterString [0..*] + registerUrl :URL [0..*] «featureType» SoilNutrients + nutrientName :GenericName + nutrientAmount :Measure + nutrientMeasure :CharacterString «codeList» ProductKindValue + organic + mineral «dataType» SoilTextureType + clay :Percent + silt :Percent + sand :Percent «dataType» ProductionType + productionDate :Date + variety :CharacterString + productionAmount :Measure + productionAnalysis :ProductionAnalysisType [0..*] «dataType» ProductionAnalysisType + productionAnalysisDate :Date + property :Measure «codeList» OriginTypeValue + manual + system «featureType» Alert + code :Identifier + type :CharacterString [1..*] + description :CharacterString [0..1] + checkedByUser :Boolean + alertDate :Date + alertGeometry :GM_Object «featureType» Intervention + type :CharacterString + description :CharacterString + notes :CharacterString [0..1] + status :CharacterString + creationDateTime :DateTime + interventionStart :DateTime + interventionEnd :DateTime [0..1] + interventionGeometry :GM_Object [1..*] + supervisor :CI_ResponsibleParty [0..1] + operator :CI_ResponsibleParty [0..*] + evidenceParty :CI_ResponsibleParty [1..*] «featureType» TreatmentPlan + treatmentPlanCode :CharacterString [0..*] + description :CharacterString [1..*] + type :CharacterString + campaign :TM_Period [1..*] + treatmentPlanCreation :DateTime + notes :CharacterString [0..1] «featureType» ProductPreparation + productQuantity :Measure + solventQuantity :Measure [0..*] + safetyPeriod :TM_Period «dataType» ActiveIngredients + code :CharacterString [0..1] + ingredientName :CharacterString + ingredientAmount :Measure «codeList» DoseUnit + minimumDose :Measure + maximumDose :Measure «codeList» FormOfTreatmentValue + manual + applicationMachine + aerial «featureType» ManagementZone + code :Identifier + validFrom :DateTime [0..1] + validTo :DateTime [0..1] + beginLifeSpanVersion :DateTime + endLifeSpanVersion :DateTime [0..1] + geometry :GM_Object [1..*] + notes :CharacterString + dateOfAnalysis :DateTime [0..*] «featureType» SoilType + soilType :CharacterString [1..*] «featureType» SoilTexture + soilTexture :SoilTextureType [1..*] «featureType» pH + pH :Measure [1..*] «featureType» OrganicMatter + organicMatter :Percent «featureType» ElectricConductivity + electricConductivity :Measure [1..*] «featureType» ZonePolygon + area :Measure «dataType» CropType + name :CharacterString + scientificName :CharacterString + description :CharacterString + notes :CharacterString + seedingAdvice :CharacterString + harvestingAdvice :CharacterString 0..* 1..* +contains 1..* HoldingSites 1..* 0..* +containsZone 0..* +HoldingPlot 0..* +containsPlot 1..* HoldingPlots 0..* 0..1 0..1 0..* 0..* +crop 0..*
  8. 8. 8www.foodie-project.eu Presentation Outline 1. Motivation and context 2. Transformation Process 3. Ontology 4. Discussion and Future Work
  9. 9. 9www.foodie-project.eu  Semi-automatic approach* using ShapeChange tool, which • processes application schemas for geographic information from a UML model and derives implementation representations • Implements ISO 19150-2 standard defining rules for mapping ISO geographic information UML models to OWL ontologies. • Required different pre and post processing tasks Transformation from UML model to OWL ontology S. Tschirner, A. Scherp, and S. Staab. Semantic access to inspire. In Terra Cognita’11 Workshop Foundations, Technologies and Applications of the Geospatial Web. Citeseer, 2011 L. Van den Brink, P. Janssen, and W. Quak. From geo-data to linked data: Automated transformation from gml to rdf. Linked Open Data-Pilot Linked Open Data Nederland, 2013. XMIXML schemas, feature catalogs, and RDF/OWL
  10. 10. 10www.foodie-project.eu  Pre-processing tasks • Source model preparation  assignment of INSPIRE application schema stereotype, fixing inconsistencies, naming target sides of aggregations/associations • ShapeChange tool configuration  Encoding rules driving the conversion process  Mappings from UML classes to OWL elements  for the classes and properties referenced in the model  Namespaces definition for base ontologies (fixed and extended)  ISO 19100 series standards, GeoSPARQL OGC standard and INSPIRE  standard vocabularies like rdf, skos, dublin core and PROV • Base ontologies fixes  INSPIRE common ontology  Ontologies based on the ISO 19100 series standards**  drastic changes between versions, miss several elements from the standard and in most cases the ontologies are only available as OWL full ontologies Transformation from UML model to OWL ontology
  11. 11. 11www.foodie-project.eu  Preliminary ontology generated after ShapeChange execution • 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  Post-processing tasks • Manual fixes in the ontology  incorrect namespaces, missing prefixes and unnecessary imports  incorrect axiom generated to constraint the cardinality of the property rdfs:label in a class expression • Manual creation of ontology elements of the base INSPIRE schemas (AF) Transformation from UML model to OWL ontology
  12. 12. 12www.foodie-project.eu Presentation Outline 1. Motivation and context 2. Transformation Process 3. Ontology 4. Discussion and Future Work
  13. 13. 13www.foodie-project.eu  For the purposes of FOODIE, we found the lack of a feature on a more detailed level than Site that is already part of the INSPIRE AF data model.  Main concept: Plot FOODIE ontology foodie:Plot INSPIRE-AF:Site foodie:Alert Foodie:InterventionFoodie:CropSpecies Foodie:ManagementZone containsPlot containsManagementZone interventionPlotspeciesPlot alertPlot plotAlert Foodie:ProductionType production  One lower level is ManagementZone • Enables a more precise description of the land characteristics in fine- grained area • 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 Foodie:SoilNutrients zoneNutrients
  14. 14. 14www.foodie-project.eu  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 FOODIE ontology Foodie:Intervention Foodie:Treatment Foodie:TreatmentPlan Foodie:Product Foodie:ProductPreparation Foodie:ActiveIngredients is-a plan productPlan planProduct preparationProduct preparation productTreatment treatmentProduct preparationPlan ingredientProduct Foodie:FormOfT reatmentValue Foodie:Treatme ntPurposeValue formOfTreatmenttreatmentPurpose
  15. 15. 15www.foodie-project.eu FOODIE ontology The Top hierarchy The FeatureType hierarchy
  16. 16. 16www.foodie-project.eu FOODIE ontology The Datatype hierarchy The Codelist hierarchy
  17. 17. 17www.foodie-project.eu Presentation Outline 1. Motivation and context 2. Transformation Process 3. Ontology 4. Discussion and Future Work
  18. 18. 18www.foodie-project.eu  Not a straightforward process, needed substantial manual intervention  Need to clarify the issues with the ontologies based on the ISO 19100 series standard.  FOODIE ontology to be aligned with standard vocabularies, so that data can be integrated with existing sources • FAO vocabularies  AGROVOC  Agrontology • Datacube (e.g., for integration with EUROSTAT)  Source data collected and generated in compliance with FOODIE model will be made available in a relational database • FOODIE has a clustered postgres-XL (with postgis) installation • UML model has been translated into a relational schema just recently and data has started to be collected  Publication of linked data will require data conversion (e.g., data upgrade or on-the-fly query translation) Discussion & Future Work
  19. 19. Partners www.foodie-project.eu This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 621074 Thank you for your attention Raul Palma (PSNC) rpalma@man.poznan.pl http://www.foodie-project.eu http://twitter.com/foodie_project

Notas do Editor

  • (with forestry and fishing)
  • Typical farm activities carried out by farmers include the monitoring field operations, managing the finances and applying for subsidies, depending on different software applications. Farmers need to use different tools to manage monitoring and data acquisition on‐line in the field. They need to analyse information related to subsidies, and to communicate with tax offices, product resellers etc.
  • Data sources include
    farming data (maps, sampling data, yields, fertilization, etc.),
    open datasets (satellite images, agro-food statistical information, etc.),
    commercial data (VHR satellite images, ortophotos, etc.)
    voluntary data (VGI data like OpenStreetMap)
  • added automatically by the processor rules (hard-coded)
    (rdfs:label was the mapped property for the UML element ”name”) -> because
    rdfs:label is a predefined annotation property so it can only be used in annotations
  • one farming mode (conventional vs. transitional vs. organic farming)

    (i) metadata information, including properties: code (id), validity (when the plot started and ceased to exist), geometry (spatial extent), description and originType (manual, system);
  • Data upgrawhich consists in generating RDF data from the relational database (i.e., Postgres-XL) according to mapping descriptions, and store it in sematic triplestore (Virtuoso).
    On-the-fly query translation, which allows evaluating SPARQL queries over a virtual RDF dataset, by rewriting those queries into SQL according to the mapping descriptions

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