UniDL 2010 - Compatibility Formalization Between PR-OWL and OWL

(Towards) Compatibility Formalization
Between PR-OWL and OWL
Rommel Carvalho, Kathryn Laskey, and Paulo Costa
George Mason University

Paper - First International Workshop on Uncertainty in Description Logics
UniDL - FLoC 2010

Thursday, July 15, 2010

Agenda

2


Agenda

Introduction

2


Agenda

Introduction
Problem

2


Agenda

Introduction
Problem
MEBN and PR-OWL

2


Agenda

Introduction
Problem
MEBN and PR-OWL
Mapping Concepts

2


Agenda

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

2


Agenda

Introduction
Problem
MEBN and PR-OWL
Mapping Concepts
Conclusion

2


Introduction

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

4
Conclusion

Introduction
A 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.

5
Conclusion

Introduction

6
Conclusion

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

6
Conclusion

Introduction
MEBN
PR-OWL
Uses MEBN logic to provide a framework for building
probabilistic ontologies

6
Conclusion

Introduction
MEBN
PR-OWL
Uses MEBN logic to provide a framework for building
probabilistic ontologies
Simple fraud detection and prevention example for
illustrating problems/solutions

6
Conclusion

Problem

7
Conclusion

Problem

8
Conclusion

Problem

?

1. Mapping - winner
2.Types - value

8
Conclusion

MEBN and PR-OWL

9
Conclusion

MEBN

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Conclusion

MEBN
BN + FOL [15]
Procurement Procurement Directed MFrag
Fraud Detection
MTheory [2]

Directed Procurement by Indices MFrag

Indices MFrag

10
Conclusion

PR-OWL

11
Conclusion

PR-OWL
MEBN + OWL [5]

11
Conclusion

Mapping Concepts

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Conclusion

Mapping Concepts - OWL to PR-OWL

13
Conclusion

Mapping Concepts - PR-OWL to OWL

14
Conclusion

Mapping Concept Instance

15
Conclusion


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Conclusion


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Conclusion

Conclusion

18
Conclusion

Conclusion

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Conclusion

Conclusion
Described the main compatibility issues between
PR-OWL and OWL
Lack of mapping between deterministic and probabilistic
concepts
Lack of use of existing types

19
Conclusion

Conclusion
Described 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

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Conclusion

Conclusion

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Conclusion

Conclusion

Future work

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Conclusion

Conclusion

Future work
Deﬁne new syntax for PR-OWL 2

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Conclusion

Conclusion

Future work
Deﬁne semantics for PR-OWL 2

20
Conclusion

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

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Conclusion

Obrigado!

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UniDL 2010 - Compatibility Formalization Between PR-OWL and OWL

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