Brainstorming on the ways PaNET can be utilized

[gkoum]

The practical application of an ontology significantly influences the decisions made during its development. Reasoning and inference are powerful capabilities that require adherence to specific design patterns to maximize their potential. As the scope of the ontology expands toward a comprehensive and authoritative structure, both its possible applications and its complexity increase. The main evolutionary stages of an ontology are:

1. Basic Vocabulary: Establishing a simple, common vocabulary where each term encapsulates its meaning in the word used.
2. Taxonomy: Developing a taxonomy that captures hierarchical relationships (e.g., subclassOf) to enhance semantic clarity.
3. Lightweight Ontology: Introducing relationships (object and datatype properties) to further improve semantic richness.
4. Detailed Ontology: Implementing restrictions (e.g., disjoint classes and cardinality constraints) to add precision and rigor.
5. Comprehensive Ontology: Creating a complex, authoritative ontology that incorporates detailed terms and constraints to describe concepts and restrict meanings as precisely as possible.

This progressive enhancement ensures that the ontology evolves from a simple list of terms to a sophisticated, semantically rich structure capable of supporting advanced reasoning and inference.

Applications Related to the Maturity Level of the Ontology

1. Unique Identification of Terms: Assign a unique IRI (Internationalized Resource Identifier) to each term to ensure clear and unambiguous identification. This is crucial for adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles.
2. Enhanced Semantic Definitions: As the descriptive power of the ontology increases, it facilitates semantic negotiations among domain experts. Continuous dialogue and engagement help resolve ambiguities, leading to mutual understanding, effective comparison, and collaboration.
3. Integration with Other Ontologies: Connect the ontology with other established ontologies (e.g., NeXus concepts, material ontologies) to enable complex reasoning and inference across diverse data sets.
4. Development of Knowledge Bases: Transition from simple databases to advanced knowledge bases by storing data alongside ontologies (Tbox + Abox). Utilizing triple/graph stores allows for powerful reasoning and rule-based systems, unlocking deeper insights.
5. SPARQL Querying for FAIR Data: Enable federated SPARQL querying across multiple knowledge bases, advancing towards fully FAIR data. This capability supports comprehensive data integration and retrieval, enhancing research and application.

Based on the above sort description we should mention possible real life applications of PaNET in near and far future according to our needs.

[gkoum]

Regarding some needed examples that would demonstrate the applicability of PaNET I duplicated the following from PR #143:

• In the examples we need to include in the form of comments what exactly is demonstrated and what is achieved. Examples are human readable parts in the ontology development and comments can help with that.

• Instances hold the complete meaning of their class but cannot be connected to a class. What we are trying to do when creating instances is to create a Knowledge-Base(KB) exactly as you would do with a regular sql database. The ontology is actually the skeleton of the KB to be created by adding individuals exactly like the database Schema is the structure of the DB. The taxonomial way that PaNET uses makes it appropriate for providing an IRI to identify a technique but does not give so many grips for adding individuals and perform reasoning.

A nice example would be to provide queries with which someone can check if a technique is a subclass of another more general class. For instance to check if:

'energy dispersive extended x-ray absorption fine structure' isA technique that can 'obtain atomic structure' and 'obtain local coordination'

In the following is this DL query that also reveals what other techniques have these characteristics:

or even better in a sparql query: