Probabilistic Ontology: Representation and Modeling Methodology

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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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Probabilistic Ontology: Representation and Modeling Methodology

  1. 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. 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. 3. Introduction Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 3Monday, June 27, 2011
  4. 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. 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. 6. What’s the problem? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 6Monday, June 27, 2011
  7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 23. Ontology in OWL 9Monday, June 27, 2011
  24. 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. 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. 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. 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. 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. 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. 30. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  31. 31. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  32. 32. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  33. 33. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  34. 34. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  35. 35. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  36. 36. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  37. 37. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  38. 38. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  39. 39. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  40. 40. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 13Monday, June 27, 2011
  41. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 52. Representing Uncertainty in Semantic Technologies Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 17Monday, June 27, 2011
  53. 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. 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. 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. 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. 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. 58. MEBN Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 20Monday, June 27, 2011
  59. 59. MEBN Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 20Monday, June 27, 2011
  60. 60. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  61. 61. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  62. 62. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  63. 63. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  64. 64. Why MEBN? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 21Monday, June 27, 2011
  65. 65. PR-OWL 1.0 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 22Monday, June 27, 2011
  66. 66. PR-OWL 1.0 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 22Monday, June 27, 2011
  67. 67. UnBBayes - MEBN / PR-OWL 1.0 Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 23Monday, June 27, 2011
  68. 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. 69. 1st Problem - Mapping/Types Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 25Monday, June 27, 2011
  70. 70. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 25Monday, June 27, 2011
  71. 71. 1st Problem - Mapping/Types ? Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 25Monday, June 27, 2011
  72. 72. Mapping Schema PR-OWL OWL Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  73. 73. Mapping Schema PR-OWL OWL Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  74. 74. Mapping Schema PR-OWL OWL hasEducationLevel Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 26Monday, June 27, 2011
  75. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 111. Methodology Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 36Monday, June 27, 2011
  112. 112. Modeling Cycle - Procurement Fraud Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  113. 113. Modeling Cycle - Procurement Fraud Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  114. 114. Modeling Cycle - Procurement Fraud Introduction - Representing Uncertainty in ST - PR-OWL 2.0 - UMP-ST - Conclusion 37Monday, June 27, 2011
  115. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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