PEAR: a Tool for Reasoning About Scenarios and Probabilities in - PowerPoint PPT Presentation

Description Logics of Typicality DLs with Typicality and Probabilities PEAR Conclusions PEAR: a Tool for Reasoning About Scenarios and Probabilities in Description Logics of Typicality Gian Luca Pozzato and Gabriele Soriano 1 1 Dipartimento di Informatica, Universit´ a degli Studi di Torino, Italy CILC 2019 Gian Luca Pozzato

Introduction Description Logics of Typicality Prototypical Reasoning DLs with Typicality and Probabilities Description Logics of Typicality PEAR Monotonic semantics ALC + T Conclusions The nonmonotonic semantics PEAR Outline Extensions of DLs with Typicality and Probabilities: Reasoning about ABox facts with probabilities of exceptions PEAR: a reasoner for DL + T + probabilities Gian Luca Pozzato

Introduction Description Logics of Typicality Prototypical Reasoning DLs with Typicality and Probabilities Description Logics of Typicality PEAR Monotonic semantics ALC + T Conclusions The nonmonotonic semantics Description Logics Description Logics Important formalisms of knowledge representation Two key advantages: well-defined semantics based on first-order logic good trade-off between expressivity and complexity at the base of languages for the semantic (e.g. OWL) Knowledge bases Two components: TBox=inclusion relations among concepts Dog ⊑ Mammal ABox= instances of concepts and roles = properties and relations among individuals Dog ( saki ) Gian Luca Pozzato

Introduction Description Logics of Typicality Prototypical Reasoning DLs with Typicality and Probabilities Description Logics of Typicality PEAR Monotonic semantics ALC + T Conclusions The nonmonotonic semantics Extensions of DLs DLs with nonmonotonic features need of representing prototypical properties and of reasoning about defeasible inheritance handle defeasible inheritance needs the integration of some kind of nonmonotonic reasoning mechanism DLs + MKNF DLs + circumscription DLs + default all these methods present some difficulties ... Gian Luca Pozzato

Introduction Description Logics of Typicality Prototypical Reasoning DLs with Typicality and Probabilities Description Logics of Typicality PEAR Monotonic semantics ALC + T Conclusions The nonmonotonic semantics DLs with typicality What are they? (with Laura Giordano, V. Gliozzi, N. Olivetti) Non-monotonic extensions of Description Logics for reasoning about prototypical properties and inheritance with exceptions Basic idea: to extend DLs with a typicality operator T T ( C ) singles out the “most normal” instances of the concept C semantics of T defined by a set of postulates that are a restatement of Lehmann-Magidor axioms of rational logic R Basic notions A KB comprises assertions T ( C ) ⊑ D T ( Dog ) ⊑ Affectionate means “normally, dogs are affectionate” T is nonmonotonic C ⊑ D does not imply T ( C ) ⊑ T ( D ) Gian Luca Pozzato

Introduction Description Logics of Typicality Prototypical Reasoning DLs with Typicality and Probabilities Description Logics of Typicality PEAR Monotonic semantics ALC + T Conclusions The nonmonotonic semantics The logic ALC + T min Example T ( Pig ) ⊑ ¬ FireBreathing T ( Pig ⊓ Pokemon ) ⊑ FireBreathing Reasoning ABox: Pig ( tepig ) Expected conclusions: ¬ FireBreathing ( tepig ) Gian Luca Pozzato

Introduction Description Logics of Typicality Prototypical Reasoning DLs with Typicality and Probabilities Description Logics of Typicality PEAR Monotonic semantics ALC + T Conclusions The nonmonotonic semantics The logic ALC + T min Example T ( Pig ) ⊑ ¬ FireBreathing T ( Pig ⊔ Pokemon ) ⊑ FireBreathing Reasoning ABox: Pig ( tepig ) , Pokemon ( tepig ) Expected conclusions: FireBreathing ( tepig ) Gian Luca Pozzato

Introduction Description Logics of Typicality Prototypical Reasoning DLs with Typicality and Probabilities Description Logics of Typicality PEAR Monotonic semantics ALC + T Conclusions The nonmonotonic semantics The logic ALC + T Semantics M = � ∆ I , <, . I � additional ingredient: preference relation among domain elements < is an irreflexive, transitive, modular and well-founded relation over ∆ I : for all S ⊆ ∆ I , for all x ∈ S , either x ∈ Min < ( S ) or ∃ y ∈ Min < ( S ) such that y < x Min < ( S ) = { u : u ∈ S and ∄ z ∈ S s.t. z < u } Semantics of the T operator: ( T ( C )) I = Min < ( C I ) Gian Luca Pozzato

Introduction Description Logics of Typicality Prototypical Reasoning DLs with Typicality and Probabilities Description Logics of Typicality PEAR Monotonic semantics ALC + T Conclusions The nonmonotonic semantics Weakness of monotonic semantics Logic ALC + T The operator T is nonmonotonic, but... The logic is monotonic If KB | = F , then KB’ | = F for all KB’ ⊇ KB Example in the KB of the previous slides: if Pig ( tepig ) ∈ ABox, we are not able to: assume that T ( Pig )( tepig ) infer that ¬ FireBreathing ( tepig ) Gian Luca Pozzato

PEAR: a Tool for Reasoning About Scenarios and Probabilities in - PowerPoint PPT Presentation

Description Logics of Typicality DLs with Typicality and Probabilities PEAR Conclusions PEAR: a Tool for Reasoning About Scenarios and Probabilities in Description Logics of Typicality Gian Luca Pozzato and Gabriele Soriano 1 1 Dipartimento di

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