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URSW 2013 - UMP-ST plug-in

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Presentation given by Rommel N. Carvalho at the 9th International Workshop on Uncertainty Reasoning for the Semantic Web at the 12th International Semantic Web Conference in October 21, 2013, Sydney, …

Presentation given by Rommel N. Carvalho at the 9th International Workshop on Uncertainty Reasoning for the Semantic Web at the 12th International Semantic Web Conference in October 21, 2013, Sydney, Australia. This was a joint work between the Research and Strategic Information Directorate from Brazil's Office of the Comptroller General and the Department of Computer Science from the University of Brasília.

Title: UMP-ST plug-in: a tool for documenting, maintaining, and evolving probabilistic ontologies.

Abstract: Although several languages have been proposed for dealing with uncertainty in the Semantic Web (SW), almost no support has been given to ontological engineers on how to create such probabilistic ontologies (PO). This task of modeling POs has proven to be extremely difficult and hard to replicate. This paper presents the first tool in the world to implement a process which guides users in modeling POs, the Uncertainty Modeling Process for Semantic Technologies (UMP-ST). The tool solves three main problems: the complexity in creating POs; the difficulty in maintaining and evolving existing POs; and the lack of a centralized tool for documenting POs. Besides presenting the tool, which is implemented as a plug-in for UnBBayes, this papers also presents how the UMP-ST plug-in could have been used to build the Probabilistic Ontology for Procurement Fraud Detection and Prevention in Brazil, a proof-of-concept use case created as part of a research project at the Brazilian Office of the Comptroller General (CGU).

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  • 1. UMP-ST plug-in: a tool for documenting, maintaining, and evolving probabilistic ontologies Rommel N. Carvalho, Henrique A. da Rocha, and Gilson L. Mendes Brazilian Office of the Comptroller General Marcelo Ladeira, Rafael M. de Souza, and Shou Matsumoto Universidade de Brasília ! Paper - Uncertainty Reasoning for the Semantic Web URSW - ISWC 10/21/2013 - Sydney, Australia
  • 2. Agenda 2
  • 3. Agenda Introduction 2
  • 4. Agenda Introduction UMP-ST 2
  • 5. Agenda Introduction UMP-ST UnBBayes Plug-in Architecture 2
  • 6. Agenda Introduction UMP-ST UnBBayes Plug-in Architecture UMP-ST Plug-in Use Case 2
  • 7. Agenda Introduction UMP-ST UnBBayes Plug-in Architecture UMP-ST Plug-in Use Case Conclusion 2
  • 8. Introduction Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 3
  • 9. Logic + Uncertainty Big Bang Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 4
  • 10. Logic + Uncertainty Big Bang In the last decade there has been a significant increase in formalisms that integrate uncertainty representation into ontology languages: PR-OWL [5–7], PR-OWL 2 [4, 3], OntoBayes [20], BayesOWL [8], and probabilistic extensions of SHIF(D) and SHOIN(D) [15] among others. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 4
  • 11. 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; Properties of those entities; firstName, Relationships among entities; Person, Procurement, Enterprise, ... lastName, procurementNumber, ... motherOf, ownerOf, isFrontFor ... Processes and events that happen with those entities; Statistical regularities that characterize the domain; analyzing if requirements 
 are met, choosing better proposal, ... Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 12. 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; Properties of those entities; firstName, Relationships among entities; Person, Procurement, Enterprise, ... lastName, procurementNumber, ... motherOf, ownerOf, isFrontFor ... Processes and events that happen with those entities; Statistical regularities that characterize the domain; analyzing if requirements 
 are met, choosing better proposal, ... Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 13. 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; Properties of those entities; firstName, Relationships among entities; Person, Procurement, Enterprise, ... lastName, procurementNumber, ... motherOf, ownerOf, isFrontFor ... Processes and events that happen with those entities; Statistical regularities that characterize the domain; analyzing if requirements 
 are met, choosing better proposal, ... Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 14. 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; Properties of those entities; firstName, Relationships among entities; Person, Procurement, Enterprise, ... lastName, procurementNumber, ... motherOf, ownerOf, isFrontFor ... Processes and events that happen with those entities; Statistical regularities that characterize the domain; analyzing if requirements 
 are met, choosing better proposal, ... Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 15. 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; Properties of those entities; firstName, Relationships among entities; Person, Procurement, Enterprise, ... lastName, procurementNumber, ... motherOf, ownerOf, isFrontFor ... Processes and events that happen with those entities; Statistical regularities that characterize the domain; analyzing if requirements 
 are met, choosing better proposal, ... Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; P(isFrontFor| Uncertainty about all the above forms of knowledge; valueOfProcurement = >1M, 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 [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 16. Probabilistic Ontology My objective is to define and represent a context model for the interoperability of Sensor is an explicit, formal knowledge representation A probabilistic ontology Networks. As my background is about a domain of application. This includes: that expresses knowledge not computer science, it's Types of entities that being a little hard to exist in the domain; Person, Procurement, Enterprise, ... understand how to put in Properties of those entities; firstName, lastName, procurementNumber, ... practice a probabilistic ontology. Relationships among entities; motherOf, ownerOf, isFrontFor ... PhD student, Wageningen University, The Netherlands Processes and events that happen with those entities; Statistical regularities that characterize the domain; analyzing if requirements 
 are met, choosing better proposal, ... Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; P(isFrontFor| Uncertainty about all the above forms of knowledge; valueOfProcurement = >1M, 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 [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 17. Probabilistic Ontology 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 is an explicit, formal knowledge representation we try to design using A probabilistic ontology Networks. As my background is about a domain of application. This includes: UnBBayes, the questions we that expresses knowledge not computer science, it's are trying to answer is how do Types of entities that being a little hard to exist in the domain; Person, Procurement, Enterprise, ... you identify which entities understand how to put in are relevant to the problem Properties of those entities; firstName, lastName, procurementNumber, ... practice a probabilistic and how translate them as ontology. among entities; motherOf, ownerOf, in your system. variables isFrontFor ... Relationships The Netherlands PhD student, Wageningen University, Fusion Engineer, EADS Innovation Works, UK Processes and events that happen with those entities; Statistical regularities that characterize the domain; analyzing if requirements 
 are met, choosing better proposal, ... Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; P(isFrontFor| Uncertainty about all the above forms of knowledge; valueOfProcurement = >1M, 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 [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 18. Probabilistic Ontology 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 is an explicit, formal knowledge representation we try to design using A probabilistic ontology Networks. As my background is about a domain of application. This includes: UnBBayes, the questions we that expresses knowledge not computer science, it's are trying to answer is how do Types of entities that being a little hard to exist in the domain; Person, Procurement, Enterprise, ... you identify which entities understand how to put in are relevant to the problem Properties of those entities; firstName, lastName, procurementNumber, ... practice a probabilistic and how translate them as ontology. among entities; motherOf, ownerOf, in your system. variables isFrontFor ... Relationships The Netherlands PhD student, Wageningen University, Fusion Engineer, EADS Innovation Works, UK analyzing if requirements 
 are met, choosing better proposal, ... Processes and events that happenawith those entities; I am evaluating PR-OWL as knowledge representation as Statistical regularities that characterize the domain; well as reasoning formalism. I'd like to explore if/how it can Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities be used of the domain; for applications P(isFrontFor| using resource devices. valueOfProcurement = >1M, PhD student, University of the Arlington, USA Uncertainty about allTexas atabove 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 [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 19. Probabilistic Ontology 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 is an explicit, formal knowledge representation we try to design using A probabilistic ontology Networks. As my background is about a domain of application. This includes: UnBBayes, the questions we that expresses knowledge not computer science, it's are trying to answer is how do Types of entities that being a little hard to exist in the domain; Person, Procurement, Enterprise, ... you identify which entities understand how to put in are relevant to the problem Properties of those entities; firstName, lastName, procurementNumber, ... practice a probabilistic and how translate them as ontology. among entities; motherOf, ownerOf, in your system. variables isFrontFor ... Relationships The Netherlands PhD student, Wageningen University, Fusion Engineer, EADS Innovation Works, UK analyzing if requirements 
 entities; are met, choosing better proposal, Why use these variables?... Processes and events that happenawith those I am evaluating PR-OWL as knowledge representation as Statistical regularities that characterize the domain; Why they are connected in well as reasoning formalism. such a way? How do you I'd like to ambiguous, incomplete, unreliable, and dissonant knowledge related to entities explore if/how it can Inconclusive, choose what type of be used of the domain; for applications P(isFrontFor| variable it is? using resource devices. valueOfProcurement = Works, UK Fusion Engineer, EADS Innovation >1M, PhD student, University of the Arlington, USA Uncertainty about allTexas atabove 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 [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 20. Probabilistic Ontology 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 is an explicit, formal knowledge representation we try to design using A probabilistic ontology Networks. As my background is about a domain of application. This includes: UnBBayes, the questions we that expresses knowledge not computer science, it's are trying to answer is how do One thing which might be beyond the Person,of this tutorial is a scope Types of entities to exist in the domain; identifyProcurement, Enterprise, ... that being a little hard you which entities write-up about "Art of Modeling with MEBN". Both narration and understand how to put in are relevant to the problem the resultant MEBN help in understanding the problem, but Properties of those entities; firstName, lastName, procurementNumber, ... practice a probabilistic and how translate them as how one reach from a problem description to a MEBN at ontology. among entities; variables isFrontFor your system. ownerOf, Relationships not very clear. motherOf, Fusion Engineer, in toInnovation Works, UK times is The Netherlands ... So when it comes MEBN,... how PhD student, Wageningen University, EADS one decides aboutthat happenawith those entities; analyzing if requirements 
 are met, Processes and events the context nodes, input nodes and resident I am evaluating PR-OWL as nodes? Most of the times choosing better proposal, Why use these variables?... knowledge representation as it might be pretty obvious but sometimes it is that characterize Statistical regularities not very clear why domain; nodes arethey are connected in Why modeled well as reasoning formalism. the certain as input explore if/how it can fragment when they could also be a way? How do you such modeled I'd like to nodes in aincomplete, unreliable, and dissonant knowledge related to entities Inconclusive, ambiguous, etc. Should we follow an object-oriented type of as contextfor applications choose what be used nodes, of the domain; P(isFrontFor| approach when identifying important entities or should we it is? variable using resource devices. valueOfProcurement = Works, UK Fusion Engineer, EADS think inabout allTexas predicate logic, etc.? As a annualIncome = Innovation = 90% terms of atabove forms of knowledge; modeler what <10k) >1M, PhD student, Uncertainty University of the Arlington, USA drives our thinking process? Professor, Institute of Business (real or fictitious, where the term entity refers to any concept Administration, Pakistan concrete or abstract) that can be described and reasoned about within the domain of application [5]. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 5
  • 21. Our Goal Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 6
  • 22. Our Goal Uncertainty Modeling Process for Semantic Technologies (UMP-ST) Describes the main tasks involved in creating probabilistic ontologies. But it is only a guideline for ontology designers. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 6
  • 23. Our Goal Uncertainty Modeling Process for Semantic Technologies (UMP-ST) Describes the main tasks involved in creating probabilistic ontologies. But it is only a guideline for ontology designers. UMP-ST plug-in overcomes three main problems: the complexity in creating probabilistic ontologies; the difficulty in maintaining and evolving existing probabilistic ontologies; and the lack of a centralized tool for documenting probabilistic ontologies. Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 6
  • 24. UMP-ST Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 7
  • 25. Methodology Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 8
  • 26. Modeling Cycle - Procurement Fraud Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 27. Modeling Cycle - Procurement Fraud Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 28. Modeling Cycle - Procurement Fraud Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 29. 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 30. 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 31. 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 32. 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 33. 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 34. 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 35. 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 9
  • 36. UMP-ST Plug-in 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 10
  • 37. UMP-ST Plug-in 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 “Requirements traceability refers to the ability to describe and follow the life of a requirement, in both forward and backward directions.” [11] 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 10
  • 38. UnBBayes Plug-in Architecture Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 11
  • 39. UnBBayes Plug-in Framework Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 12
  • 40. UnBBayes UMP-ST Plug-in Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 13
  • 41. UMP-ST Plug-in Use Case Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 14
  • 42. ⇤ ii) Procure por um membro da comissão e um resp participante da licitação que vivam no mesmo endere Requirements As figuras 5.1 e 5.2 trazem uma parte da GUI do UMP-ST plugin Goal: Find suspicious procurements Query: Is there any relation between the committee andrelacionadas ao dois objetivos em ques de visualização das hipóteses the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Figura 5.1: Painel de hipóteses do primeiro objeti Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 15
  • 43. ⇤ ii) Procure por um membro da comissão e um resp participante da licitação que vivam no mesmo endere Requirements As figuras 5.1 e 5.2 trazem uma parte da GUI do UMP-ST plugin Goal: Find suspicious procurements Query: Is there any relation between the committee andrelacionadas ao dois objetivos em ques de visualização das hipóteses the enterprises that participated in the procurement? Evidence: They are siblings They live at the same address Figura 5.1: Painel de hipóteses do primeiro objeti Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 15
  • 44. , o relatórioJudicialCriminal, que tem informações sobre o veredicto. Analysis figuras. Foram criadas 4 atributos: as entidades não aparecem nas& Design - Entities pessoa), 2.Valor (referente ao contrato), 3. estaSuspenso (relativo a Person Procurement ntoAnual (relativo a impostoDeRenda). Como citado na abertura Enterprise as algumas telas serão apresentadas nesta monografia. Caso o leitor ownerOf r em detalhes todas as outras entidades, relacionamentos e atributos participatesIn livesAt m CD contendo todas as telas. gura 5.3: Painel de Figura 5.4: Painel de relacionamentos do UMP-ST plugin podem ser determinísticas ou não determinísticas (que envolvem probabilidade). A darei apenas as regras não determinísticas, uma vez que as regras determinísticas d entidadesestão resumidas a relações de cardinalidade e unicidade. ontologia do UMP-ST plugin 1. Se - UMP-ST UnBBayes Plug-in Architecture Introductionum membro- do comitê tiver um parente (pai, 
 mãe, irmão ou irmã) respons 16 por descrever os requisitos - Conclusion UMP-ST Plug-in Use Case da licitação, então há mais chances de haver uma rela entre comitê e empresa, o que inibe a concorrência.
  • 45. , o relatórioJudicialCriminal, que tem informações sobre o veredicto. Analysis figuras. Foram criadas 4 atributos: as entidades não aparecem nas& Design - Entities pessoa), 2.Valor (referente ao contrato), 3. estaSuspenso (relativo a Person Procurement ntoAnual (relativo a impostoDeRenda). Como citado na abertura Enterprise as algumas telas serão apresentadas nesta monografia. Caso o leitor ownerOf r em detalhes todas as outras entidades, relacionamentos e atributos participatesIn livesAt m CD contendo todas as telas. gura 5.3: Painel de Figura 5.4: Painel de relacionamentos do UMP-ST plugin podem ser determinísticas ou não determinísticas (que envolvem probabilidade). A darei apenas as regras não determinísticas, uma vez que as regras determinísticas d entidadesestão resumidas a relações de cardinalidade e unicidade. ontologia do UMP-ST plugin 1. Se - UMP-ST UnBBayes Plug-in Architecture Introductionum membro- do comitê tiver um parente (pai, 
 mãe, irmão ou irmã) respons 16 por descrever os requisitos - Conclusion UMP-ST Plug-in Use Case da licitação, então há mais chances de haver uma rela entre comitê e empresa, o que inibe a concorrência.
  • 46. Analysis & Design - Rules 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 17
  • 47. Analysis & Design - Rules 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 - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 17
  • 48. Analysis & Design - Groups Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 18
  • 49. Analysis & Design - Groups Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 18
  • 50. Analysis & Design - Traceability Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 19
  • 51. Conclusion Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 20
  • 52. Conclusion Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 21
  • 53. Conclusion First tool in the world to implement UMP-ST Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 21
  • 54. Conclusion First tool in the world to implement UMP-ST Also the first in the world to support the design of POs Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 21
  • 55. Conclusion First tool in the world to implement UMP-ST Also the first in the world to support the design of POs A GUI tool for designing, maintaining, and evolving POs Overcomes the complexity in creating POs by providing a step by step guidance Provides a centralized tool for documenting POs Provides a constant attention to where and what your changes might impact through the implementation of requirements traceability Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 21
  • 56. Future Work Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 22
  • 57. Future Work More tests (still a beta tool) Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 22
  • 58. Future Work More tests (still a beta tool) Exporting all documentation to a single PDF of HTML file Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 22
  • 59. Future Work More tests (still a beta tool) Exporting all documentation to a single PDF of HTML file Generating MFrags automatically based on the groups defined in the last step of the Analysis & Design discipline, in order to facilitate the creation of a MEBN model (i.e., PR-OWL PO) during the Implementation discipline Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 22
  • 60. Future Work More tests (still a beta tool) Exporting all documentation to a single PDF of HTML file Generating MFrags automatically based on the groups defined in the last step of the Analysis & Design discipline, in order to facilitate the creation of a MEBN model (i.e., PR-OWL PO) during the Implementation discipline Apply same methodology to different PO languages Introduction - UMP-ST - UnBBayes Plug-in Architecture - 
 UMP-ST Plug-in Use Case - Conclusion 22
  • 61. Obrigado! 23