OntoBella – An Ontology of Knowledge and Belief
Representation of knowledge and beliefs plays a crucial role in Artificial Intelligence, especially in modeling artificial agents being able to carry out actions on the basis of their knowledge about environment and goals and in automated planning. Beliefs are largely studied in epistemic logic, where many formal systems of representation of private, public and common beliefs have been proposed. One special interest is a question of belief revision formally described by the well-known AGM model. Finally, the important role of knowledge representation in argumentation and persuasion is evident.
Nonetheless, ontological foundations of these approaches remain unclear. The domain of beliefs did not attract a lot of attention in ontological engineering despite the substantial research in psychology and philosophy. The authors of this contribution are not aware of any domain specific ontology of beliefs except for the COM ontology. We present a slightly different approach to modelling the domain of beliefs.
Our ontology of beliefs, Ontobella, has two main sources of inspiration:
1. R. Ingarden’s ontology of intentional object (cf. ), in particular:
- the distinction between autonomous and heteronomous entities,
- the conception of intentional objects as contents of mental acts,
- the definition of beliefs as those mental acts the represent situations together with his theory of situations/states of affairs (Sachverhalten).
2. the psychological legacy of the Lvov-Warsaw school (the so-called ”descriptive psychology”), in particular:
- the conception of beliefs as perdurants,
- the thesis that representational object exhibit (mental) content,
- the distinction between assertions and rejections as non-reducible propositional attitudes,
- the definition of memory and expectation.
Representation of beliefs in Ontobella
- Do you still want to vote for your favorite politician? Ask Ontobella!. In: FOMI, pp. 102-113, 2009.
- Inżynieryjna ontologia przekonań Ontobella. In: Grzech,; Juszczyn,; Kwasnicka,; Nguyen, (Ed.): Inżynieria Wiedzy i Systemy Ekspertowe, Akademicka Oficyna Wydawnicza EXIT, 2009.
- Tadeusza Czeżowskiego koncepcja przekonań a filozoficzne podstawy inżynieryjnej ontologii przekonań. In: Ruch Filozoficzny, 1 , pp. 709–724, 2009.
We created a formalization of one of the leading ontologies in Knowledge Management by the name of Formal Knowledge Management Ontology. Our logical theory was named Formalised Formal Knowledge Management Ontology (F2KMO). We specify in it the primitive, that is, undefined terms we assume, the axioms whose role is to fix the meanings of those terms, and a number of definitions that correspond to the definitions one may find in the latest exposition of Formal Knowledge Management Ontology. We show that F2KMO is consistent and prove its usefulness by defining the logical schema of a database based on the theory.
- A formal ontology of knowing and knowledge. In: Knowledge Management Research & Practice, 10 , pp. 206–-226, 2012.
- Using Perseus system for modelling epistemic interactions. In: Transactions on Computational Collective Intelligence LNCS, 5 , pp. 106–123, 2011, ISBN: 978-3-642-24015-7.
- Epistemic capacities, incompatible information and incomplete beliefs. In: ILCLI International Workshop on Logic and Philosophy of Knowledge, Communication and Action (LogKCA-10), 2010.
- Ontologia w rozwiązywaniu zadań. In: DiaLogikon, pp. 63–84, Wydawnictwo Uniwersytetu Jagiellońskiego, 2010.
- Using Perseus system for modelling epistemic interactions. In: P. Jedrzejowicz N. T. Nhuyen, Howlett; Jain, (Ed.): Agent and Multi-Agent Systems: Technologies and Applications, 4th International Symposium, KES-AMSTA 2010, Part I, pp. 205–216, Springer, 2010, ISBN: 978-3-642-13479-1.
- A formal model for epistemic interactions. In: Nguyen,; Katarzyniak,; Janiak, (Ed.): New Challenges in Computational Collective Intelligence, pp. 205–216, Springer, 2009, ISBN: 978-3-642-03957-7.
- Beyond Public Announcement Logic, An Alternative Approach to some AI Puzzles. In: Mertsching,; Hund,; Aziz, (Ed.): KI 2009: Advances in Artificial Intelligence, pp. 379–-386, Springer, 2009.