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).

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
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48. Analysis Design - Groups
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
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49. Analysis Design - Groups
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
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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
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56. Future Work
Introduction - UMP-ST - UnBBayes Plug-in Architecture -
UMP-ST Plug-in Use Case - Conclusion
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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