Probabilistic Ontology: Representation and Modeling Methodology

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Oral Defense of Doctoral DissertationVolgenau School of Engineering, George Mason UniversityRommel Novaes CarvalhoBachelor of Science, University of Braslia, Brazil, 2003Master of Science, University of Braslia, Brazil, 2008Probabilistic Ontology: Representation and Modeling MethodologyTuesday, June 28, 2011, 2:00pm -- 4:00pmNguyen Engineering Building, Room 4705CommitteeKathryn Laskey, ChairPaulo CostaKuo-Chu ChangDavid SchumLarry KerschbergFabio CozmanAbstract The past few years have witnessed an increasingly mature body of research on the Semantic Web (SW), with new standards being developed and more complex problems being addressed. As complexity increases in SW applications, so does the need for principled means to cope with uncertainty in SW applications. Several approaches addressing uncertainty representation and reasoning in the SW have emerged. Among these is Probabilistic Web Ontology Language (PR-OWL), which provides Web Ontology Language (OWL) constructs for representing Multi-Entity Bayesian Network (MEBN) theories. However, there are several important ways in which the initial version PR-OWL 1.0 fails to achieve full compatibility with OWL. Furthermore, although there is an emerging literature on ontology engineering, little guidance is available on the construction of probabilistic ontologies.This research proposes a new syntax and semantics, defined as PR-OWL 2.0, which improves compatibility between PR-OWL and OWL in two important respects. First, PR-OWL 2.0 follows the approach suggested by Poole et al. to formalizing the association between random variables from probabilistic theories with the individuals, classes and properties from ontological languages such as OWL. Second, PR-OWL 2.0 allows values of random variables to range over OWL datatypes.To address the lack of support for probabilistic ontology engineering, this research describes a new methodology for modeling probabilistic ontologies called Uncertainty Modeling Process for Semantic Technologies (UMP-ST). To better explain the methodology and to verify that it can be applied to different scenarios, this dissertation presents step-by-step constructions of two different probabilistic ontologies. One is used for identifying frauds in public procurements in Brazil and the other is used for identifying terrorist threats in the maritime domain. Both use cases demonstrate the advantages of PR-OWL 2.0 over its predecessor.

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  • 1.Probabilistic Ontology:Representation and Modeling Methodology Rommel Novaes Carvalho Dissertation DefensePhD in Systems Engineering and Operations Research George Mason University 06/28/2011Monday, June 27, 2011

2. AgendaIntroductionProblem StatementContributionsRepresenting Uncertainty in Semantic Technologies1st Major Contribution: PR-OWL 2.02nd Major Contribution: Uncertainty Modeling Processfor Semantic Technologies (UMP-ST)Conclusion 2Monday, June 27, 2011 3. Introduction Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 3Monday, June 27, 2011 4. Summary of Problems 1st major problem - Mapping/TypesProbabilistic web ontology language (PR-OWL) does not have a well-dened and complete integration between the deterministic andprobabilistic parts of an ontology 2nd major problem - MethodologyProbabilistic languages for semantic technologies like PR-OWL lack amethodology for guiding the construction of models Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 4Monday, June 27, 2011 5. Summary of Contributions For the 1st problem - Mapping/TypesExtended probabilistic web ontology language (PR-OWL)Led the development of a proof of concept tool in collaboration withUnB [105]* For the 2nd problem - MethodologyDeveloped a methodology for modeling probabilisticontologies (POs)Created two use cases using the proposed methodology*These numbers refer to the references in my dissertation Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 5Monday, June 27, 2011 6. Whats the problem? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011 7. Whats the problem? Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011 8. Whats the problem?Information Gathering Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011 9. Whats the problem?Information GatheringDB - Information Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011 10. Whats the problem?> 1 Bi info> 5 Tri US$Information GatheringDB - Information Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion6Monday, June 27, 2011 11. Whats the problem?> 1 Bi info> 5 Tri US$Information GatheringDB - Information Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion6Monday, June 27, 2011 12. Whats the problem?> 1 Bi info> 5 Tri US$Information GatheringDB - Information Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion6Monday, June 27, 2011 13. Whats the problem?Information GatheringDB - Information Public Notices - DataDesign - UnBBayes Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011 14. Whats the problem?Information GatheringDB - Information Public Notices - Data Design - UnBBayes Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011 15. Whats the problem?Information GatheringDB - Information Public Notices - Data Design - UnBBayesReport for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011 16. Whats the problem?Information GatheringDB - Information Public Notices - Data Design - UnBBayesReport for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011 17. Semantic WebSemantic Web (SW) is a web of data that can beprocessed by machines [45]E.g., allow machines to differentiate between 3 pounds (price ofproduct) and 3 pounds (weight of product)Change focus from data driven to knowledge drivenThe World Wide Web Consortium (W3C) states thatontologies provide the cement for building the SW [46]Ontology: Taken from Philosophy, where it means a systematicexplanation of being Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 7Monday, June 27, 2011 18. Ontology An ontology is 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 ctitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL)Developed by the W3CAs a language to represent ontologies for the SWAccepted as a W3C recommendation in 2004 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 8Monday, June 27, 2011 19. Ontology An ontology is an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:Types of entities that exist in the domain; Person, Procurement, Enterprise, ...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 ctitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL)Developed by the W3CAs a language to represent ontologies for the SWAccepted as a W3C recommendation in 2004 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 8Monday, June 27, 2011 20. Ontology An ontology is an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:Types of entities that exist in the domain; Person, Procurement, Enterprise, ...Properties of those entities; firstName, lastName, procurementNumber, ...Relationships among entities;Processes and events that happen with those entities; where the term entity refers to any concept (real or ctitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL)Developed by the W3CAs a language to represent ontologies for the SWAccepted as a W3C recommendation in 2004 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 8Monday, June 27, 2011 21. Ontology An ontology is an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:Types of entities that exist in the domain; Person, Procurement, Enterprise, ...Properties of those entities; firstName, lastName, procurementNumber, ...Relationships among entities;motherOf, ownerOf, isFrontFor ...Processes and events that happen with those entities; where the term entity refers to any concept (real or ctitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL)Developed by the W3CAs a language to represent ontologies for the SWAccepted as a W3C recommendation in 2004 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 8Monday, June 27, 2011 22. Ontology An ontology is an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:Types of entities that exist in the domain; Person, Procurement, Enterprise, ...Properties of those entities; firstName, lastName, procurementNumber, ...Relationships among entities;motherOf, ownerOf, isFrontFor ...analyzing if requirementsProcesses and events that happen with those entities; are met,choosing best proposal, ... where the term entity refers to any concept (real or ctitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL)Developed by the W3CAs a language to represent ontologies for the SWAccepted as a W3C recommendation in 2004 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 8Monday, June 27, 2011 23. Ontology in OWL9Monday, June 27, 2011 24. Uncertainty in the SWThe community recognizes the need to representand reason with uncertaintyW3C created the URW3-XG in 2007 Concluded that standardized representations were needed [50]Probabilistic Web Ontology Language (PR-OWL)is a candidate language to represent probabilisticontologies Based on Multi-Entity Bayesian Network (MEBN) logic Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 10Monday, June 27, 2011 25. Probabilistic Ontology A probabilistic ontology is an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:Types of entities that exist in the domain;Person, Procurement, Enterprise, ...Properties of those entities;firstName, lastName, procurementNumber, ...Relationships among entities; motherOf, ownerOf, isFrontFor ...analyzing if requirementsProcesses and events that happen with those entities; are met,choosing better proposal, ...Statistical regularities that characterize the domain;Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entitiesof the domain;Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or ctitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 11Monday, June 27, 2011 26. Probabilistic Ontology A probabilistic ontology is an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:Types of entities that exist in the domain;Person, Procurement, Enterprise, ...Properties of those entities;firstName, lastName, procurementNumber, ...Relationships among entities; motherOf, ownerOf, isFrontFor ...analyzing if requirementsProcesses and events that happen with those entities; are met,choosing better proposal, ...Statistical regularities that characterize the domain;Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entitiesof the domain;Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or ctitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 11Monday, June 27, 2011 27. Probabilistic Ontology A probabilistic ontology is an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:Types of entities that exist in the domain;Person, Procurement, Enterprise, ...Properties of those entities;firstName, lastName, procurementNumber, ...Relationships among entities; motherOf, ownerOf, isFrontFor ...analyzing if requirementsProcesses and events that happen with those entities; are met,choosing better proposal, ...Statistical regularities that characterize the domain;Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entitiesof the domain;Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or ctitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 11Monday, June 27, 2011 28. Probabilistic Ontology A probabilistic ontology is an explicit, formal knowledge representation that expresses knowledge about a domain of application. This includes:Types of entities that exist in the domain;Person, Procurement, Enterprise, ...Properties of those entities;firstName, lastName, procurementNumber, ...Relationships among entities; motherOf, ownerOf, isFrontFor ...analyzing if requirementsProcesses and events that happen with those entities; are met,choosing better proposal, ...Statistical regularities that characterize the domain;Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entitiesof the domain;P(isFrontFor|valueOfProcurement = >1M,Uncertainty about all the above forms of knowledge; annualIncome = 50% 60.08% ...support 3hypothesis...(b) isSuspiciousCommittee (b) 10% < P(H = true | E) < 50% (b) P(H = true | E) = (procurement) 28.95%Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion38Monday, June 27, 2011 123. Modeling Cycle - MDAGO FASTER ON THISSLIDE! JUST SAY THAT IUSED THE SAMEMETHODOLOGY ON ADIFFERENT DOMAIN.Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011 124. Modeling Cycle - MDAGO FASTER ON THISSLIDE! JUST SAY THAT IUSED THE SAMEMETHODOLOGY ON ADIFFERENT DOMAIN.Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011 125. Modeling Cycle - MDAGO FASTER ON THISSLIDE! JUST SAY THAT IUSED THE SAMEMETHODOLOGY ON ADIFFERENT DOMAIN.Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011 126. Modeling Cycle - MDAGoal: Identify whether a ship is a ship of interestQuery: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist;Crew member associated with terroristorganizationGO FASTER ON THISSLIDE! JUST SAY THAT IUSED THE SAMEMETHODOLOGY ON ADIFFERENT DOMAIN.Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011 127. Modeling Cycle - MDAGoal: Identify whether a ship is a ship of interestQuery: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist;Crew member associated with terroristorganization Ship Person Organization isTerroristPersonGO FASTER ON THIShasCrewMemberSLIDE! JUST SAY THAT I isRelatedToUSED THE SAMEMETHODOLOGY ON ADIFFERENT DOMAIN.Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011 128. Modeling Cycle - MDAGoal: Identify whether a ship is a ship of interestQuery: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist;Crew member associated with terroristorganization Ship Person Organization isTerroristPersonGO FASTER ON THIShasCrewMemberSLIDE! JUST SAY THAT I isRelatedToUSED THE SAMEMETHODOLOGY ON AIf a crew member is related to aDIFFERENT DOMAIN. terrorist, then it is more likely that he is also a terroristIntroduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion39Monday, June 27, 2011 129. Modeling Cycle - MDAGoal: Identify whether a ship is a ship of interestQuery: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist;Crew member associated with terroristorganization Ship...

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