unidl 2010 - compatibility formalization between pr-owl and owl

(Towards) Compatibility Formalization Between PR-OWL and OWLRommel Carvalho, Kathryn Laskey, and Paulo Costa

George Mason University

Paper - First International Workshop on Uncertainty in Description LogicsUniDL - FLoC 2010

Thursday, July 15, 2010

Agenda

Introduction

Agenda

Introduction

Problem

Agenda

Introduction

Problem

MEBN and PR-OWL

Agenda

Introduction

Problem

MEBN and PR-OWL

Mapping Concepts

Agenda

Introduction

Problem

MEBN and PR-OWL

Mapping Concepts

Using Existing Types

Agenda

Introduction

Problem

MEBN and PR-OWL

Mapping Concepts

Conclusion

Introduction

3Introduction - Problem - MEBN and PR-OWL - Mapping Concepts - Using Existing Types -

Conclusion

Introduction

An ontology is [5] an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:

Types of entities that exist in the domain;

Properties of those entities;

Relationships among entities;

Processes and events that happen with those entities;

where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application.

Conclusion

IntroductionA probabilistic ontology is [5] an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:

Types of entities that exist in the domain;

Properties of those entities;

Relationships among entities;

Processes and events that happen with those entities;

Statistical regularities that characterize the domain;

Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain;

Uncertainty about all the above forms of knowledge;

where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application.

Conclusion

Introduction

Conclusion

Introduction

Represent and reason with uncertainty about any propositions that can be expressed in first-order logic

Conclusion

Introduction

PR-OWL

Uses MEBN logic to provide a framework for building probabilistic ontologies

Conclusion

Introduction

PR-OWL

Uses MEBN logic to provide a framework for building probabilistic ontologies

Simple fraud detection and prevention example for illustrating problems/solutions

Conclusion

Problem

Conclusion

Problem

Conclusion

Problem

Conclusion

Problem

Conclusion

Problem

1. Mapping - winner2. Types - value

Introduction - Problem - MEBN and PR-OWL - Mapping Concepts - Using Existing Types - Conclusion

MEBN and PR-OWL

Conclusion

BN + FOL [15]

Indices MFrag

Directed Procurement by Indices MFrag

Procurement Directed MFragProcurement Fraud Detection

MTheory [2]

PR-OWL

Conclusion

PR-OWL

MEBN + OWL [5]

Mapping Concepts

Conclusion

Mapping Concepts - OWL to PR-OWL

Conclusion

Mapping Concepts - PR-OWL to OWL

Conclusion

Mapping Concept Instance

Conclusion

ConclusionDescribed the main compatibility issues between PR-OWL and OWL

Lack of mapping between deterministic and probabilistic concepts

Lack of use of existing types

Conclusion

ConclusionDescribed the main compatibility issues between PR-OWL and OWL

Lack of mapping between deterministic and probabilistic concepts

Lack of use of existing types

Provided qualitative descriptions of possible solutions

Random Variable closely related to Properties

Substitution of PR-OWL’s types by OWL’s types

Conclusion

Future work

Conclusion

Future work

Define new syntax for PR-OWL 2

Conclusion

Future work

Define semantics for PR-OWL 2

Conclusion

Future work

Define semantics for PR-OWL 2

Present problem and possible solutions for consistency checking between OWL axioms and PR-OWL local probability distributions

Conclusion

Obrigado!

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