DFKI-LT - Capturing Graded Knowledge and Uncertainty in a Modalized Fragment of OWL
Capturing Graded Knowledge and Uncertainty in a Modalized Fragment of OWL
1 Proceedings of the 8th International Conference on Agents and Artificial Intelligence, Rome, Italy, INSTICC, INSTICC, 2016,
Conference Website: http://www.icaart.org
Natural language statements uttered in diagnosis (e.g., in medicine), but more general in daily life are usually graded, i.e., are associated with a degree of uncertainty about the validity of an assessment and is often expressed through specific verbs, adverbs, or adjectives in natural language. In this paper, we look into a representation of such graded statements by presenting a simple non-standard modal logic which comes with a set of modal operators, directly associated with the words indicating the uncertainty and interpreted through confidence intervals in the model theory. We complement the model theory by a set of RDFS-/OWL 2 RL-like entailment (if-then) rules, acting on the syntactic representation of modalized statements. Our interest in such a formalization is related to the use of OWL as the de facto language in today's ontologies and its weakness to represent and reason about assertional knowledge that is uncertain or that changes over time.
Files: BibTeX, gradedmodals.pdf