PEAR: A tool for reasoning about scenarios and probabilities in description logics of typicality

In this work we describe PEAR, a tool for reasoning about prototypical properties in an extension of Description Logics of typicality with probabilities and scenarios. PEAR implements a non-monotonic procedure for the logic ALC+TPR, a recently introduced extension of the logic of typicality ALC+TR by inclusions of the form T(C) ?p D, where p is a real number between 0 and 1 capturing the intuition that “all the typical Cs are Ds, and the probability that a C is not a D is 1 − p”. In this logic, different scenarios are considered by taking into account several extension of the ABox, containing only some typicality assumptions about individuals. Each scenario has a probability depending on those equipping typicality inclusions, then entailment can be restricted to scenarios whose probabilities belong to a given and fixed range. PEAR is implemented in Python, it computes all scenarios of a knowledge base and it allows the user to check the probability of a query by exploiting a translation into standard ALC.