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INTALA LAB Ontologies Hub
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Ontology development methodology

The ontologies published through INTALA LAB Ontologies Hub are developed using a structured methodology that combines domain analysis, semantic modelling, ontology engineering principles, and web-based publication practices.

  1. Domain analysis

    Each ontology begins with a systematic analysis of the target domain, such as apiculture, hazelnut production, smart agriculture, livestock systems, or agricultural decision support. Domain concepts, actors, processes, resources, environmental factors, and decision-making needs are identified before formal modelling.

  2. Competency questions

    Competency questions are used to define the expected scope and reasoning capability of each ontology. These questions guide the identification of classes, properties, and relationships that the ontology should be able to represent.

  3. Ontology design

    Ontologies are modelled using classes, object properties, data properties, individuals, and annotations. Class hierarchies are designed to represent domain taxonomies, while properties define semantic relationships among entities and their attributes.

  4. IRI and namespace policy

    Each ontology is assigned a persistent base IRI under the INTALA LAB ontology domain. The general pattern is:

    https://ontologies.intalalab.net/ontology/{ontology-id}

    Entity IRIs are generated using namespace IRIs with hash-based identifiers:

    https://ontologies.intalalab.net/ontology/{ontology-id}#ClassName

  5. Versioning strategy

    Ontology files are maintained using version-specific directories. This enables reproducibility, long-term access, and clear tracking of ontology evolution. 

    /ontology-files/{ontology-id}/v1.0/{ontology-id}.rdf

  6. RDF/XML publication

    Ontologies are primarily published in RDF/XML format to ensure compatibility with ontology editors, RDF parsers, SPARQL engines, and semantic web tools. Additional serializations such as Turtle may be provided in future versions.

  7. Validation and quality control

    Each ontology is checked for syntactic correctness, consistent IRI usage, class/property structure, and compatibility with RDF-based query processing. The platform also supports basic structural inspection through entity counts, SPARQL queries, and visualization tools.

  8. Reuse and extensibility

    The ontologies are designed to support reuse in research projects, decision support systems, knowledge graphs, semantic data integration, and smart agriculture applications. Future versions may extend the models with additional concepts, relationships, datasets, and reasoning capabilities.