Formal ontologies are increasingly used in a variety of domains in crucial applications of AI, as well as in Multiagent Systems, Conceptual Modelling, Database Design, NLP and Software Engineering.
Ontologies are a way to express the information about a certain domain in a peculiar way: they intend to make the modelling choices and the assumptions of the modeler clear, justified, and sharable among the community of users.
Formal ontological analysis aims at eliciting and formalizing the implicit ontological foundations of a body of knowledge, i.e., the nature and structure of the world that justifies such knowledge, in terms of very general categories and relations.
For instance, notions like object, property, relation, event, time, space, quality, modality, disposition, and so on, are at the core of formal ontological analysis. Nowadays, these general notions are systematized in foundational ontologies, which have been constructed by means of a tight confrontation with the literature in linguistics, cognitive science, logic, and analytic philosophy, and provide a well-developed theory for comprehending and justifying the modeller’s choices. Well-known foundational ontologies are DOLCE, BFO, and UFO. In this tutorial, we shall focus on DOLCE and UFO (those whose design we have been personally involved in), analyzing their motivations, their similarities and their differences, and the practical ways of using them for ontological analysis and knowledge representation.
This tutorial is addressed to all the practitioners of AI whose work requires a grounded description of a particular domain, and is intended to provide the necessary tools to assess the ontological adequacy of representational choices.
Structure of the tutorial :
A – The interplay between ontology and knowledge representation:
1. Introduction: Applied Ontology, ontological analysis, and knowledge representation (slides 1-5)
2. What is an ontology – Ontologies and conceptualizations (short) (slides 6-13)
3. The role of ontological precision and accuracy (slides 14-23)
4. Intro to formal tools of ontological analysis (24-26)
5. The ontological level (27-33)
B – OntoUML: OntoUML: modeling at the ontological level.
C – WHAT to represent? Ontological analysis as a search for truth-makers.
8. Quantification, ontological commitment, and truth-making
9. Reification and truthmaking
D. Truth-making patterns: a systematic approach
E. Final discussion