OKP4 Knowledge Sharing through Ontology-Driven Dataverse: A New Approach to Knowledge Description

The OKP4 protocol aims to revolutionize data sharing and knowledge creation by enabling heterogeneous systems and resources to communicate with each other. Based on various technologies, including blockchain, decentralized storage, cloud computation, and governance concepts such as Data Space and Dataverse, all these elements lack a conceptual framework so that anyone can fully understand, appropriate, and use the power of OKP4.

This is where ontology comes in - a formal and structured representation of the concepts, relationships, and properties of the protocol.

1) Recalls and Definitions

1.1 | Ontology, one representation among others

In computer science, ontology is a formal and structured representation of the concepts, relationships, and properties of a particular domain. For OKP4, ontology is essential as it enables the description of shared knowledge. Participants can better understand and interpret the exchanged information, even if they come from different backgrounds.

This ontology allows us to achieve:

1. Standardization of terminology: standardized terminology is used for concepts and relationships in a given domain, clarifying and avoiding misunderstandings between participants.
2. Structuring of data: data is structured in a coherent and organized way, making it easier to access, process, and analyze.
3. Interoperability of systems and tools: a well-designed ontology enables interoperability between systems and tools, facilitating the sharing of knowledge among different stakeholders.
4. Improved data research and analysis by accurately describing concepts and relationships in a particular domain.

1.2 | Other representations

Other approaches and representations exist for organizing the concepts of the OKP4 protocol. However, most of these alternatives are less precise and less structured and therefore cannot fully embrace the conceptual complexity of OKP4.

These include:

• Taxonomy: a hierarchical classification method based on predefined categories. Taxonomy is simple to use but difficult to represent complex relationships.
• Folksonomy: a collaborative classification method based on tags or keywords. Folksonomy is simple to use but lacks structure and coherence, limiting search and analysis capabilities.
• Semantic networks: a representation based on nodes and links that represent concepts and their relationships. The lack of formalism and precision also limits the ability to represent complex relationships.
• Artificial intelligence (NLP): technology that aims to analyze and understand human language using automatic text processing techniques. However, these technologies are dependent on textual corpus and can be biased and struggle to represent complex relationships.

1.3 | Ontology: Concepts, Languages, and Main Representations

An ontology generally comprises the following basic elements: concepts, relationships, properties, axioms, and instances. These can be graphically represented by the simplified equation shown below.

Some definitions:

• Concepts: represent the main formalized elements of the domain.
• Relationships: represent links between concepts.
• Properties: represent attributes or characteristics that are associated with concepts.
• Axioms: represent logical statements or rules that define relationships between concepts, properties, and instances, ensuring the consistency and coherence of the knowledge represented within the ontology.
• Instances: represent the concrete instances of concepts representing objects in the application domain.

Some examples of ontology:

An example of ontology with animals:

An example of ontology with water and hydrological system:

Several languages are generally used to represent ontologies, including OWL, RDF, RDFS, SKOS, etc. OWL, RDF, RDFS, and SKOS are described in more detail below.

• OWL (Web Ontology Language): a standard language of the World Wide Web Consortium (W3C) for representing ontologies. OWL is based on descriptive logic and allows for the definition of classes, subclasses, properties, and relationships.
• RDF (Resource Description Framework): a markup language for representing information about resources on the Web, including ontologies. RDF describes resources in terms of their properties and relationships with other resources.
• RDFS (RDF Schema): an ontology representation language that defines classes and properties and relationships between them. RDFS is an extension of RDF.
• SKOS (Simple Knowledge Organization System): a language for representing ontology that allows the description of classification systems and thesauri. SKOS allows the definition of concepts, relationships, and properties.

Here is the semantic representation of a RDF stack:

2) The OKP4 Ontology

2.1 | Designing an Ontology for the OKP4 Protocol

The creation and design of an ontology for OKP4 requires a global understanding of all the concepts carried by the protocol. These concepts are all explained in the Whitepaper. The OKP4 protocol's GitHub repository also allows for the appropriation and reuse of OKP4 ontology elements (https://github.com/okp4/ontology - CC-BY-SA-4.0 license).

Here is a simplified functional diagram of the OKP4 protocol.

In summary, the OKP4 protocol orchestrates the various resources of the Dataverse (datasets and services) using different blockchain elements such as smart contracts, logic modules, and ontology. All these elements allow for fine management of dataset and service workflows for knowledge creation within a Data Space with personalized governance. As seen in Part 1, the ontology must stand for the different concepts of the protocol, their relationships, and their properties.

Here is a schematic representation of the OKP4 ontology.

2.2 | Details of the OKP4 Ontology

The following concepts and properties are found within the OKP4 ontology:

• Dataset
- hasIdentifier
This is a dataset made available by a user on the protocol. Datasets are subject to regulation by consents, which define rules and constraints to dictate how they can be accessed and used.

• Dataset Core Description
- hasTag
- hasCreator
- hasDescription
- hasPublisher
- hasTitle
- hasSpatialCoverage
- hasTemporalCoverage
This is the description of a given dataset in metadata form.

• Zone
A Zone is regulated by Governance. Governance defines the regulatory elements and rules that oversee all activities and interactions within the Zone.

• DIDURI
A decentralized identifier URI. A URI that identifies a subject in a decentralized system and is managed independently of any centralized registry.

• Services
- hasTag
- hasCreator
- hasDescription
- hasPublisher
- hasTitle
A service consumes a resource and produces data. Services are subject to regulation by consents, which define rules and constraints to dictate how they can be accessed and used.

With all these concepts, their properties, and their relationships, we can create the OKP4 ontology and explain the workings of the OKP4 protocol in a structured and formalized way. This ontology can be expressed in different formats, more or less understandable by humans or machines. It can be expressed in French or English, RDF, OWL, JSON-LD, N-Triples, Notation3 RDF/XML, Turtle, etc.

2.3 | Ontology construction process

The construction of this ontology follows a number of steps which are described below:

1. Ontology scope definition(1) & knowledge acquisition(2): Identification and definition of key concepts and relationships in the domain of interest and the terms that refer to such concepts, in natural language.
2. Ontology specification(3) & conceptualization(4): Formalizing of the elements identified in the previous step in the form of a knowledge representation, using the building blocks of ontologies: classes, attributes, relationships, subsumption.
3. Ontology implementation(5): Encoding the ontology according to the OWL grammar.
4. Ontology evaluation(6): Association of key concepts and terms in the ontology with concepts and terms of other ontologies.

3) Conclusion

The OKP4 protocol requires a representation system for dissemination and adoption by the widest possible audience. This system must be powerful enough to grasp the complexity of the protocol, its concepts, and their relationships, but also be understandable by humans and machines. The ontology is extensible, by its capacity to scale and evolve. After analyzing the needs of OKP4, the different types of representations, and the different languages, ontology is the most suitable representation for a precise, structured, and shareable description of a complex protocol like OKP4. This ontology allows us to have:

• Unified terminology
• Structured and organized knowledge
• Interoperability of systems
• Efficient research and analysis
• Facilitated appropriation for builders
• Possible alignment with potential future standards (IDSA, Gaia-X, etc.)
• Future perspectives and challenges for the use of ontology in knowledge sharing
• An evolving structure

The main challenge for this ontology will now be its ability to be used and understood by the builders of the protocol. One of the challenges will also be to be able to evolve and adapt to new concepts that will be produced to improve and optimize the protocol. As the protocol is in perpetual motion by nature, its ontology will have to adapt.