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Probabilistic Ontology: Representation and Modeling Methodology

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Oral Defense of Doctoral Dissertation …

Oral Defense of Doctoral Dissertation

Volgenau School of Engineering, George Mason University

Rommel Novaes Carvalho

Bachelor of Science, University of Brasília, Brazil, 2003
Master of Science, University of Brasília, Brazil, 2008

Probabilistic Ontology: Representation and Modeling Methodology
Tuesday, June 28, 2011, 2:00pm -- 4:00pm
Nguyen Engineering Building, Room 4705


Committee
Kathryn Laskey, Chair
Paulo Costa
Kuo-Chu Chang
David Schum
Larry Kerschberg
Fabio Cozman


Abstract
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 Defense PhD in Systems Engineering and Operations Research George Mason University 06/28/2011Monday, June 27, 2011
  • 2. Agenda Introduction Problem Statement Contributions Representing Uncertainty in Semantic Technologies 1st Major Contribution: PR-OWL 2.0 2nd Major Contribution: Uncertainty Modeling Process for 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/Types Probabilistic web ontology language (PR-OWL) does not have a well- defined and complete integration between the deterministic and probabilistic parts of an ontology 2nd major problem - Methodology Probabilistic languages for semantic technologies like PR-OWL lack a methodology 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/Types Extended probabilistic web ontology language (PR-OWL) Led the development of a proof of concept tool in collaboration with UnB [105]* For the 2nd problem - Methodology Developed a methodology for modeling probabilistic ontologies (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. What’s the problem? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 7. What’s the problem? Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 8. What’s the problem? Information Gathering Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 9. What’s the problem? Information Gathering DB - Information Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 10. What’s the problem? > 1 Bi info > 5 Tri US$ Information Gathering DB - Information Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 11. What’s the problem? > 1 Bi info > 5 Tri US$ Information Gathering DB - Information Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 12. What’s the problem? > 1 Bi info > 5 Tri US$ Information Gathering DB - Information Public Notices - Data Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 13. What’s the problem? Information Gathering DB - Information Public Notices - Data Design - UnBBayes Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 14. What’s the problem? Information Gathering DB - 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. What’s the problem? Information Gathering DB - Information Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 16. What’s the problem? Information Gathering DB - Information Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  • 17. Semantic Web Semantic Web (SW) is a web of data that can be processed by machines [45] E.g., allow machines to differentiate between 3 pounds (price of product) and 3 pounds (weight of product) Change focus from data driven to knowledge driven The World Wide Web Consortium (W3C) states that ontologies provide the cement for building the SW [46] Ontology: Taken from Philosophy, where it means a systematic explanation 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 fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL) Developed by the W3C As a language to represent ontologies for the SW Accepted 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 fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL) Developed by the W3C As a language to represent ontologies for the SW Accepted 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 fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL) Developed by the W3C As a language to represent ontologies for the SW Accepted 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 fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. The Web Ontology Language (OWL) Developed by the W3C As a language to represent ontologies for the SW Accepted 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 requirements Processes and events that happen with those entities; are met, choosing best proposal, ... 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 [2]. The Web Ontology Language (OWL) Developed by the W3C As a language to represent ontologies for the SW Accepted 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 OWL 9Monday, June 27, 2011
  • 24. Uncertainty in the SW The community recognizes the need to represent and reason with uncertainty W3C 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 probabilistic ontologies 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 requirements Processes 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 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 [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 requirements Processes 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 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 [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 requirements Processes 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 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 [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 requirements Processes 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 entities of the domain; P(isFrontFor| valueOfProcurement = >1M, Uncertainty about all the above forms of knowledge; annualIncome = <10k) = 90% 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 [2]. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 11Monday, June 27, 2011
  • 29. Probabilistic Ontology in PR-OWL 1.0 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 12Monday, June 27, 2011
  • 30. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 31. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 32. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 33. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 34. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 35. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 36. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 37. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 38. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 39. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 40. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  • 41. How to build Probabilistic Ontologies? Information Gathering DB - Information Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 42. How to build Probabilistic Ontologies? Information Gathering DB - Information Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 43. How to build Probabilistic Ontologies? Information Gathering DB - Information Logic + Uncertainty Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 44. How to build Probabilistic Ontologies? Information Gathering DB - Information ? Logic + Uncertainty Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 45. How to build Probabilistic Ontologies? My objective is to define and represent a context model for the interoperability of Sensor Networks. As my background is not computer science, its being a little hard to Information Gathering understand how to put in DB - Information ? practice a probabilistic ontology. PhD student, Wageningen University, The Netherlands Logic + Uncertainty Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 46. How to build Probabilistic Ontologies? This seems a very promising My objective is to define and tool, but we need to learn how represent a context model for to best make use of it. When the interoperability of Sensor we try to design using Networks. As my background is UnBBayes, the questions we not computer science, its are trying to answer is how do being a little hard to you identify which entities Information Gathering understand how to put in DB - Information are relevant to the problem ? practice a probabilistic and how translate them as ontology. variables Logic system. in your PhD student, Wageningen University, The Netherlands Fusion Engineer, EADS Innovation Works, UK + Uncertainty Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 47. How to build Probabilistic Ontologies? This seems a very promising My objective is to define and tool, but we need to learn how represent a context model for to best make use of it. When the interoperability of Sensor we try to design using Networks. As my background is UnBBayes, the questions we not computer science, its are trying to answer is how do being a little hard to you identify which entities Information Gathering understand how to put in DB - Information are relevant to the problem ? practice a probabilistic and how translate them as ontology. variables Logic system. in your PhD student, Wageningen University, The Netherlands Fusion Engineer, EADS Innovation Works, UK I am evaluating PR-OWL as a + knowledge representation as Uncertainty well as reasoning formalism. Public Notices Id like to explore if/how it can - Data Design - UnBBayes be used for applications using resource devices. PhD student, University of Texas at Arlington, USA Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 48. How to build Probabilistic Ontologies? This seems a very promising My objective is to define and tool, but we need to learn how represent a context model for to best make use of it. When the interoperability of Sensor we try to design using Networks. As my background is UnBBayes, the questions we not computer science, its are trying to answer is how do being a little hard to you identify which entities Information Gathering understand how to put in DB - Information are relevant to the problem ? practice a probabilistic and how translate them as ontology. variables Logic system. in your PhD student, Wageningen University, The Netherlands Fusion Engineer, EADS Innovation Works, UK I am evaluating PR-OWL as a + knowledge representation as Uncertainty these variables? Why use well as reasoning formalism. Why they are connected in Public Notices Id like to explore if/how it can - Data such a way? How -do you Design UnBBayes be used for applications choose what type of using resource devices. variable it is? Fusion Engineer, EADS Innovation Works, UK PhD student, University of Texas at Arlington, USA Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 49. How to build Probabilistic Ontologies? This seems a very promising My objective is to define and tool, but we need to learn how represent a context model for to best make use of it. When the interoperability of Sensor we try to design using Networks.One thing which might be beyond the scope of the questions we As my background is UnBBayes, this tutorial is a not computer science, itsof Modeling with MEBN". to answer is how do write-up about "Art are trying Both narration and being a little hard to the resultant MEBN help in understanding the problem, but you identify which entities how one reach from a problem descriptionInformation at Information Gathering understand how to put in DB - to the problem are relevantto a MEBN ? practice a probabilistic clear. ... So when it how translate them as times is not very and comes to MEBN, how one decides about the context nodes, input nodes and system. ontology. variables Logic resident in your PhD student, Wageningen University, The Netherlands nodes? Most of the times it mightFusion Engineer, EADS Innovationbut UK be pretty obvious Works, I am evaluating PR-OWL as why certain nodes + modeled sometimes it is not very cleara are knowledge representation when they could also be modeled variables? as input nodes in a fragmentas Uncertainty these Why use as context nodes, etc. Should we follow an object-oriented connected in well as reasoning formalism. Why they are Id like to when identifying important entities or should we How -do you Public Notices approach explore if/how it can - Data such a way? Design UnBBayes think used for applications logic, etc.? As a modeler what be in terms of predicate choose what type of using resource devices. drives our thinking process? variable it is? Fusion Engineer, EADS Innovation Works, UK PhD student, University of Texas at Arlington, USA Professor, Institute of Business Administration, Pakistan Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 14Monday, June 27, 2011
  • 50. 2nd Problem - Methodology There is now substantial literature about what PR-OWL is [2, 4, 5], how to implement it [6-9], and where it can be used [10-15] There is an emerging literature on ontology engineering [4, 28] But, little has been written about how to model a probabilistic ontology This lack of methodology is not only associated with PR-OWL OntoBayes [30], BayesOWL [31], P-SHIF(D) and P-SHOIN(D) [32], Markov Logic Network [33], Bayesian Logic [63], and Probabilistic Relational Models [64], amongst others, do not have a methodology for creating models Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 15Monday, June 27, 2011
  • 51. Contributions For the 1st problem - Mapping/Types Extended probabilistic web ontology language (PR-OWL) in order to: Provide a mapping between deterministic knowledge and probabilistic knowledge Allow reuse of existing types provided by OWL Led the development of a proof of concept tool in collaboration with UnB [105] For the 2nd problem - Methodology Developed a methodology for modeling probabilistic ontologies (POs) Created two use cases using the proposed methodology Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 16Monday, June 27, 2011
  • 52. Representing Uncertainty in Semantic Technologies Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 17Monday, June 27, 2011
  • 53. Uncertainty in the SW Deterministic SW will either consider a statement to be true, false, or unknown Shortcoming: no built-in support for uncertainty In open world partial (not complete) or approximate (not exact) information is more the rule than the exception Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 18Monday, June 27, 2011
  • 54. Uncertainty in the SW Deterministic SW will either consider a statement to be true, false, or unknown Shortcoming: no built-in support for uncertainty In open world partial (not complete) or approximate (not exact) information is more the rule than the exception ∀x,y,z ((Mother(x,y) ∧ Mother(z,y)) Sibling(x,z)) ∀x,y (Sibling(x,y) Related(x,y)) ∀y∃x,z,r Committee(x,y) ∧ Participant(z,y) ∧ Responsible(r,z) ∧ Related(x,r) ViolationOfLaw(y) Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 18Monday, June 27, 2011
  • 55. Uncertainty in the SW Deterministic SW will either consider a statement to be true, false, or unknown Shortcoming: no built-in support for uncertainty In open world partial (not complete) or approximate (not exact) information is more the rule than the exception Committee(John,procurement34) Participant(ITBusiness,procurement34) ∀x,y,z ((Mother(x,y) ∧ Mother(z,y)) Sibling(x,z)) Responsible(Richard,ITBusiness) ∀x,y (Sibling(x,y) Related(x,y)) ∀y∃x,z,r Committee(x,y) Mother(John,Jane) ∧ Participant(z,y) ∧ Responsible(r,z) Mother(Richard,Jane) ∧ Related(x,r) ViolationOfLaw(y) Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 18Monday, June 27, 2011
  • 56. Uncertainty in the SW Deterministic SW will either consider a statement to be true, false, or unknown Shortcoming: no built-in support for uncertainty In open world partial (not complete) or approximate (not exact) information is more the rule than the exception Committee(John,procurement34) Participant(ITBusiness,procurement34) ? ∀x,y,z ((Mother(x,y) ∧ Mother(z,y)) Sibling(x,z)) Responsible(Richard,ITBusiness) ∀x,y (Sibling(x,y) Related(x,y)) LivesAt(John,Address1) ∀y∃x,z,r Committee(x,y) LivesAt(Richard,Address1) ∧ Participant(z,y) ∧ Responsible(r,z) LastName(John,White) LastName(Richard,White) ∧ Related(x,r) ViolationOfLaw(y) Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 18Monday, June 27, 2011
  • 57. Uncertainty in the SW The community recognizes the need to represent and reason with uncertainty W3C created the URW3-XG in 2007 Concluded that standardized representations were needed [50] PR-OWL is a candidate language to represent probabilistic ontologies Based on MEBN logic Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 19Monday, June 27, 2011
  • 58. MEBN Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 20Monday, June 27, 2011
  • 59. MEBN Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 20Monday, June 27, 2011
  • 60. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  • 61. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  • 62. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  • 63. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  • 64. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  • 65. PR-OWL 1.0 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 22Monday, June 27, 2011
  • 66. PR-OWL 1.0 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 22Monday, June 27, 2011
  • 67. UnBBayes - MEBN / PR-OWL 1.0 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 23Monday, June 27, 2011
  • 68. 1st Major Contribution PR-OWL 2.0 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 24Monday, June 27, 2011
  • 69. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 25Monday, June 27, 2011
  • 70. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 25Monday, June 27, 2011
  • 71. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 25Monday, June 27, 2011
  • 72. Mapping Schema PR-OWL OWL Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 73. Mapping Schema PR-OWL OWL Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 74. Mapping Schema PR-OWL OWL hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 75. Mapping Schema PR-OWL OWL hasEducationLevel Person Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 76. Mapping Schema PR-OWL OWL hasEducationLevel Person EducationLevel hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 77. Mapping Schema PR-OWL OWL hasEducationLevel Person EducationLevel hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 78. Mapping Schema PR-OWL hasEducationLevel_RV OWL hasEducationLevel Person EducationLevel hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 79. Mapping Schema PR-OWL hasEducationLevel_RV hasEducationLevel_RV_person OWL hasEducationLevel Person EducationLevel hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 80. Mapping Schema PR-OWL hasEducationLevel_RV hasEducationLevel_RV_person OWL _MFrag.person hasEducationLevel Person EducationLevel hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 81. Mapping Schema PR-OWL hasEducationLevel_RV hasEducationLevel_RV_person OWL _MFrag.person hasEducationLevel aspiresEducationLevel Person EducationLevel hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 82. Mapping Schema PR-OWL hasEducationLevel_RV hasEducationLevel_RV_person OWL _MFrag.person hasEducationLevel aspiresEducationLevel domain Person EducationLevel hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 83. Mapping Schema PR-OWL hasEducationLevel_RV hasEducationLevel_RV_person OWL _MFrag.person hasEducationLevel aspiresEducationLevel domain range Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 84. Mapping Schema PR-OWL hasEducationLevel_RV hasEducationLevel_RV_person OWL _MFrag.person Person hasEducationLevel hasEducationLevel ? aspiresEducationLevel domain range EducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 85. Mapping Schema PR-OWL hasEducationLevel_RV hasEducationLevel_RV_person OWL _MFrag.person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 86. Mapping Schema PR-OWL hasEducationLevel_RV hasArgument hasEducationLevel_RV_person hasEducationLevel_RV_advisor OWL _MFrag.person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 87. Mapping Schema PR-OWL hasEducationLevel_RV “2”^^int hasArgument hasArgNumber hasEducationLevel_RV_person hasEducationLevel_RV_advisor OWL _MFrag.person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 88. Mapping Schema PR-OWL hasEducationLevel_RV “2”^^int hasArgument hasArgNumber hasEducationLevel_RV_person hasEducationLevel_RV_advisor hasArgTerm _MFrag.advisor OWL _MFrag.person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 89. Mapping Schema PR-OWL hasEducationLevel_RV “2”^^int hasArgument hasArgNumber hasEducationLevel_RV_person hasEducationLevel_RV_advisor hasArgTerm _MFrag.advisor OWL _MFrag.person isSubsBy Person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 90. Mapping Schema PR-OWL hasEducationLevel_RV “2”^^int hasArgument hasArgNumber hasEducationLevel_RV_person hasEducationLevel_RV_advisor hasArgTerm _MFrag.advisor hasEducationLevelAdvisor OWL _MFrag.person isSubsBy range domain Person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 91. Mapping Schema PR-OWL hasEducationLevel_RV “2”^^int ? hasArgument hasArgNumber hasEducationLevel_RV_person hasEducationLevel_RV_advisor hasArgTerm _MFrag.advisor hasEducationLevelAdvisor OWL _MFrag.person isSubsBy range domain Person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 92. Mapping Schema PR-OWL hasEducationLevel_RV “2”^^int hasArgument hasArgNumber hasEducationLevel_RV_person hasEducationLevel_RV_advisor hasArgTerm isObjectIn isSubjectIn _MFrag.advisor hasEducationLevelAdvisor OWL _MFrag.person isSubsBy range domain Person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 93. Mapping Schema PR-OWL hasEducationLevel_RV hasEducationLevel_RV_person isSubjectIn OWL _MFrag.person hasEducationLevel Person EducationLevel hasEducationLevel aspiresEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  • 94. Using Existing Types*reproduced with permission from [2] - extended version Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 27Monday, June 27, 2011
  • 95. Using Existing Types*reproduced with permission from [2] - extended version Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 27Monday, June 27, 2011
  • 96. Using Existing Types*reproduced with permission from [2] - extended version Boolean Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 27Monday, June 27, 2011
  • 97. Using Existing Types*reproduced with permission from [2] - extended version Boolean Nominal Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 27Monday, June 27, 2011
  • 98. Using Existing Types*reproduced with permission from [2] - extended version Boolean Nominal Class Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 27Monday, June 27, 2011
  • 99. Using Existing Types*reproduced with permission from [2] - extended version Boolean Nominal Class X Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 27Monday, June 27, 2011
  • 100. Using Existing Types*reproduced with permission from [2] - extended version Boolean DataType Nominal Class X Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 27Monday, June 27, 2011
  • 101. Using Existing Types*reproduced with permission from [2] - extended version Entity Boolean DataType Nominal Class X Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 27Monday, June 27, 2011
  • 102. PR-OWL 2.0 - Proof of Concept Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 28Monday, June 27, 2011
  • 103. PR-OWL 2.0 - Proof of Concept Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 29Monday, June 27, 2011
  • 104. PR-OWL 2.0 - Proof of Concept Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 30Monday, June 27, 2011
  • 105. PR-OWL 2.0 - Proof of Concept Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 31Monday, June 27, 2011
  • 106. PR-OWL 2.0 - Proof of Concept Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 32Monday, June 27, 2011
  • 107. 2nd Major Contribution Uncertainty Modeling Process for Semantic Technologies (UMP-ST) Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 33Monday, June 27, 2011
  • 108. How to build Probabilistic Ontologies? Information Gathering DB - Information Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 34Monday, June 27, 2011
  • 109. How to build Probabilistic Ontologies? Information Gathering DB - Information ? Logic + Uncertainty Public Notices - Data Design - UnBBayes Report for Decision Makers Inference - Knowledge Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 34Monday, June 27, 2011
  • 110. 2nd Problem - Methodology There is now substantial literature about what PR-OWL is [2, 4, 5], how to implement it [6-9], and where it can be used [10-15] There is an emerging literature on ontology engineering [4, 28] But, little has been written about how to model a probabilistic ontology This lack of methodology is not only associated with PR-OWL OntoBayes [30], BayesOWL [31], P-SHIF(D) and P-SHOIN(D) [32], Markov Logic Network [33], Bayesian Logic [63], and Probabilistic Relation Models [64], amongst others, do not have a methodology for creating models Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 35Monday, June 27, 2011
  • 111. Methodology Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 36Monday, June 27, 2011
  • 112. Modeling Cycle - Procurement Fraud Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 113. Modeling Cycle - Procurement Fraud Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 114. Modeling Cycle - Procurement Fraud Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 115. Modeling Cycle - Procurement Fraud Goal: Find suspicious procurements Query: Is there any relation between the committee and the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 116. Modeling Cycle - Procurement Fraud Goal: Find suspicious procurements Query: Is there any relation between the committee and the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Person Procurement Enterprise ownerOf participatesIn livesAt Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 117. Modeling Cycle - Procurement Fraud Goal: Find suspicious procurements Query: Is there any relation between the committee and the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Person Procurement Enterprise ownerOf participatesIn livesAt If a member of the committee lives at the same address as a person responsible for a bidder in the procurement, a relationship is more likely to exist between the committee and the enterprises, which lowers competition. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 118. Modeling Cycle - Procurement Fraud Goal: Find suspicious procurements Query: Is there any relation between the committee and the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Person Procurement Enterprise ownerOf participatesIn livesAt If a member of the committee lives at the same address as a person responsible for a bidder in the procurement, a relationship is more likely to exist between the committee and the enterprises, which lowers competition. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 119. Modeling Cycle - Procurement Fraud Goal: Find suspicious procurements Query: Is there any relation between the committee and the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Person Procurement Enterprise ownerOf participatesIn livesAt If a member of the committee lives at the same address as a person responsible for a bidder in the procurement, a relationship is more likely to exist between the committee and the enterprises, which lowers competition. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 120. Modeling Cycle - Procurement Fraud Goal: Find suspicious procurements Query: Is there any relation between the committee and the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Person Procurement Enterprise ownerOf participatesIn livesAt If a member of the committee lives at the same address as a person responsible for a bidder in the procurement, a relationship is more likely to exist between the committee and the enterprises, which lowers competition. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 121. Modeling Cycle - Procurement Fraud Goal: Find suspicious procurements Query: Is there any relation between the committee and the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Person Procurement Enterprise ownerOf participatesIn livesAt If a member of the committee lives at the same address as a person responsible for a bidder in the procurement, a relationship is more likely to exist between the committee and the enterprises, which lowers competition. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  • 122. Evaluation - Procurement Fraud Case-based evaluation (integration test / scenarios) Scenario Hypothesis (H) Evidence (E) Expected Result Result (a) isSuspiciousProcurement (a) Low probability that (a) P(H = true | E) = (procurement) ...does not P(H = true | E) 2.35% 1 support (b) isSuspiciousCommittee hypothesis... (b) Low probability that (b) P(H = true | E) = (procurement) P(H = true | E) 2.33% (a) isSuspiciousProcurement ...does and does (a) P(H = true | E) = (procurement) not support (a) 10% < P(H = true | E) < 50% 20.82% 2 hypothesis... (b) isSuspiciousCommittee ...conflicting (b) 10% < P(H = true | E) < 50% (b) P(H = true | E) = (procurement) information... 28.95% (a) isSuspiciousProcurement (a) P(H = true | E) = (procurement) (a) P(H = true | E) > 50% 60.08% ...support 3 hypothesis... (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 - Conclusion 38Monday, June 27, 2011
  • 123. Modeling Cycle - MDA GO FASTER ON THIS SLIDE! JUST SAY THAT I USED THE SAME METHODOLOGY ON A DIFFERENT DOMAIN. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 124. Modeling Cycle - MDA GO FASTER ON THIS SLIDE! JUST SAY THAT I USED THE SAME METHODOLOGY ON A DIFFERENT DOMAIN. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 125. Modeling Cycle - MDA GO FASTER ON THIS SLIDE! JUST SAY THAT I USED THE SAME METHODOLOGY ON A DIFFERENT DOMAIN. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 126. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization GO FASTER ON THIS SLIDE! JUST SAY THAT I USED THE SAME METHODOLOGY ON A DIFFERENT DOMAIN. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 127. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson GO FASTER ON THIS hasCrewMember SLIDE! JUST SAY THAT I isRelatedTo USED THE SAME METHODOLOGY ON A DIFFERENT DOMAIN. Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 128. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson GO FASTER ON THIS hasCrewMember SLIDE! JUST SAY THAT I isRelatedTo USED THE SAME METHODOLOGY ON A If a crew member is related to a DIFFERENT DOMAIN. terrorist, then it is more likely that he is also a terrorist Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 129. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson GO FASTER ON THIS hasCrewMember SLIDE! JUST SAY THAT I isRelatedTo USED THE SAME METHODOLOGY ON A If a crew member is related to a DIFFERENT DOMAIN. terrorist, then it is more likely that he is also a terrorist Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 130. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson GO FASTER ON THIS hasCrewMember SLIDE! JUST SAY THAT I isRelatedTo USED THE SAME METHODOLOGY ON A If a crew member is related to a DIFFERENT DOMAIN. terrorist, then it is more likely that he is also a terrorist Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 131. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson GO FASTER ON THIS hasCrewMember SLIDE! JUST SAY THAT I isRelatedTo USED THE SAME METHODOLOGY ON A If a crew member is related to a DIFFERENT DOMAIN. terrorist, then it is more likely that he is also a terrorist Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 132. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson GO FASTER ON THIS hasCrewMember SLIDE! JUST SAY THAT I isRelatedTo USED THE SAME METHODOLOGY ON A If a crew member is related to a DIFFERENT DOMAIN. terrorist, then it is more likely that he is also a terrorist Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 39Monday, June 27, 2011
  • 133. Evaluation - MDA Manual case-based evaluation 4 major categories were defined: A possible bomb plan using fishing ship; A possible bomb plan using merchant ship; A possible exchange illicit cargo using fishing ship; A possible exchange illicit cargo using merchant ship. 5 variations for each scenario: “Sure” positive, “looks” positive, unsure, “looks” negative, and “sure” negative. All 20 different scenarios were analyzed by the SME and were evaluated as reasonable results (what was expected). Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 40Monday, June 27, 2011
  • 134. Evaluation - MDA Automatic case-based evaluation Used simulation tool to generate ground truth Generated reports based on simulated data Inferred result and compared with ground truth Confusion matrix with threshold of 50% Inferred/ Inferred/ ≥ 50% < 50% ≥ 50% < 50% Real Real TRUE 24 3 TRUE 88.89% 11.11% FALSE 11 577 FALSE 1.87% 98.13% Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 41Monday, June 27, 2011
  • 135. Conclusion Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 42Monday, June 27, 2011
  • 136. Contributions hasEducationLevel_RV hasEducationLevel_RV_person isSubjectIn _MFrag.person Entity Boolean hasEducationLevel DataType Nominal Class X Person hasEducationLevel EducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 43Monday, June 27, 2011
  • 137. Future Work PR-OWL 2.0 implementation [105] UMP-ST implementation [122] Scalability (MEBN reasoning) PR-OWL 2.0 sublanguages/complexity Learning (MEBN learning) RV “solved” by external tools hasAnnualIncome(person) < 50,000.00 isShipLocatedInArea(ship, area) sin(x) linearEquationValue(m,b) Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 44Monday, June 27, 2011
  • 138. Future Work - EPF for UMP-ST Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 45Monday, June 27, 2011
  • 139. Publications 46Monday, June 27, 2011
  • 140. Publications - Paper I Papers: 1. R. N. Carvalho, R. Haberlin, P. C. G. Costa, K. B. Laskey, and K.-C. Chang, “Modeling a Probabilistic Ontology for Maritime Domain Awareness,” in Proceedings of the 14th International Conference on Information Fusion, Chicago, USA, 2011. 2. P. C. G. Costa, R. N. Carvalho, K. B. Laskey, and C.Y. Park, “Evaluating uncertainty representation and reasoning in HLF systems,” in Proceedings of the 14th International Conference on Information Fusion, Chicago, USA, 2011. 3. R.N. Carvalho, K.B. Laskey, and P.C.G. Costa, “PR-OWL 2.0 - Bridging the gap to OWL semantics,” Proceedings of the 6th Uncertainty Reasoning for the Semantic Web (URSW 2010) on the 9th International Semantic Web Conference (ISWC 2010), Shanghai, China: 2010. 4. R.N. Carvalho, P.C.G. Costa, K.B. Laskey, and K. Chang, “PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies,” Proceedings of the 13th International Conference on Information Fusion, Edinburgh, UK: 2010. 5. R.N. Carvalho, K.B. Laskey, and P.C.G. Costa, “Compatibility Formalization Between PR-OWL and OWL,” Proceedings of the First International Workshop on Uncertainty in Description Logics (UniDL) on Federated Logic Conference (FLoC) 2010, Edinburgh, UK: 2010. 47Monday, June 27, 2011
  • 141. Publications - Paper II Papers: 6. P.C.G. Costa, K. Chang, K.B. Laskey, and R.N. Carvalho, “High Level Fusion and Predictive Situational Awareness with Probabilistic Ontologies,” Proceedings of the AFCEA-GMU C4I Center Symposium, George Mason University, Fairfax,VA, USA: 2010. 7. R.N. Carvalho, K.B. Laskey, P.C.G. Costa, M. Ladeira, L.L. Santos, and S. Matsumoto, “Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil,” Proceedings of the 5th Uncertainty Reasoning for the Semantic Web (URSW 2009) on the 8th International Semantic Web Conference (ISWC 2009), Chantilly,Virginia, USA: 2009. 8. P.C.G. Costa, Kuo-Chu Chang, K. Laskey, and R.N. Carvalho, “A multi- disciplinary approach to high level fusion in predictive situational awareness,” Proceedings of the 12th International Conference on Information Fusion, Seattle, Washington, USA: 2009, pp. 248-255. 9. R.N. Carvalho and KC. Chang, “A performance evaluation tool for multi- sensor classification systems,” Proceedings of the 12th International Conference on Information Fusion, Seattle, Washington, USA: 2009, pp. 1123-1130. *Best Student Paper Travel Award 48Monday, June 27, 2011
  • 142. Publications - Book Book chapters: 1. R. N. Carvalho, K. B. Laskey, and P. C. G. da Costa, “PR-OWL 2.0 - Bridging the gap to OWL semantics,” in Uncertainty Reasoning for the Semantic Web II: ISWC International Workshops, URSW 2008-2010, Revised Selected and Invited Papers, Springer-Verlag (Forthcoming). 2. R. N. Carvalho, S. Matsumoto, K. B. Laskey, P. C. G. da Costa, M. Ladeira, and L. Santos, “Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil,” in Uncertainty Reasoning for the Semantic Web II: ISWC International Workshops, URSW 2008-2010, Revised Selected and Invited Papers, Springer-Verlag (Forthcoming). 3. S. Matsumoto, R. N. Carvalho, M. Ladeira, P. C. G. da Costa, L. Santos, D. Silva, M. Onishi, and E. Machado, “UnBBayes: a Java Framework for Probabilistic Models in AI,” in Java in Academia and Research, iConcept Press (Forthcoming). 4. S. Matsumoto, R. N. Carvalho, P. C. G. da Costa, K. B. Laskey, L. L. Santos, and M. Ladeira, “Theres No More Need to be a Night OWL: on the PR-OWL for a MEBN Tool Before Nightfall,” in Introduction to the Semantic Web: Concepts, Technologies and Applications, G. Fung, Ed. iConcept Press, 2011. 5. R.N. Carvalho, K.B. Laskey, P.C.G.D. Costa, M. Ladeira, L.L. Santos, and S. Matsumoto, “UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web,” Semantic Web, INTECH, 2010, pp. 1-28. 6. R.N. Carvalho, M. Ladeira, L.L. Santos, S. Matsumoto, and P.C.G. Costa, “A GUI Tool for Plausible Reasoning in the SemanticWeb Using MEBN,” Innovative Applications in Data Mining, Nadia Nedjah, Luiza de Macedo Mourelle, Janusz Kacprzyk, 2009, pp. 17-45. 49Monday, June 27, 2011
  • 143. Publications - Journal Journal papers: 1. R.N. Carvalho, K.B. Laskey, and P.C.G. Costa, “A Formal Definition for Probabilistic Ontology - PR-OWL 2.0,” Journal of Web Semantics - JWS (Preparing). 2. R.N. Carvalho, K.B. Laskey, and P.C.G. Costa, “Uncertainty Modeling Process for Semantic Technologies,” Journal of IEEE Transactions on Knowledge and Data Engineering - TKDE (Preparing). 3. R.N. Carvalho and KC Chang, “A Performance Evaluation Tool and Analysis for Multi- Sensor Classification Systems,” Submitted to Journal of Advances in Information Fusion - JAIF, Oct., 2009 (accepted with conditions). Edited work: 1. F. Bobillo, R.N. Carvalho, P.C.G. Costa, C. dAmato, N. Fanizzi, K.B. Laskey, K.J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, and M. Pool (editors), Proceedings of the 7th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2011), Bonn, Germany, 2011, CEUR Workshop Proceedings, CEUR-WS.org: 2011 (Forthcoming). 2. F. Bobillo, R.N. Carvalho, P.C.G. Costa, C. dAmato, N. Fanizzi, K.B. Laskey, K.J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, and M. Pool (editors), Proceedings of the 6th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2010), Shanghai, China, November 2010, CEUR Workshop Proceedings, CEUR- WS.org: 2010. 50Monday, June 27, 2011
  • 144. Publications - Committee Participation in committees: 1. The 7th InternationalWorkshop on Uncertainty Reasoning for the SemanticWeb (URSW 2011) Program Committee Organizing Committee 2. The 6th InternationalWorkshop on Uncertainty Reasoning for the SemanticWeb (URSW 2010) Program Committee Organizing Committee 3. The 5th InternationalWorkshop on Uncertainty Reasoning for the SemanticWeb (URSW 2009) Program Committee 4. The 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009) Co-reviewer 5. Journal of Tourism Management 2009 Reviewer 51Monday, June 27, 2011
  • 145. Obrigado! 52Monday, June 27, 2011

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