=Paper=
{{Paper
|id=Vol-527/paper-2
|storemode=property
|title=Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil
|pdfUrl=https://ceur-ws.org/Vol-527/paper1.pdf
|volume=Vol-527
|dblpUrl=https://dblp.org/rec/conf/semweb/CarvalhoLCLSM09
}}
==Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil==
https://ceur-ws.org/Vol-527/paper1.pdf
Probabilistic Ontology and Knowledge Fusion for
Procurement Fraud Detection in Brazil
Rommel N. Carvalho1, Kathryn B. Laskey1, Paulo C. G. Costa1, Marcelo Ladeira2,
Laécio L. Santos2, and Shou Matsumoto2,
1
George Mason University
4400 University Drive
Fairfax, VA 22030-4400 USA
rommel.carvalho@gmail.com, {klaskey, pcosta}@gmu.edu
2
University of Brasilia
Campus Universitário Darcy Ribeiro
Brasilia – DF 70910-900 Brazil
mladeira@unb.br, {laecio, cardialfly}@gmail.com
Abstract. To cope with society’s demand for transparency and corruption
prevention, the Brazilian Office of the Comptroller General (CGU) has carried
out a number of actions, including: awareness campaigns aimed at the private
sector; campaigns to educate the public; research initiatives; and regular
inspections and audits of municipalities and states. Although CGU has collected
information from hundreds of different sources - Revenue Agency, Federal
Police, and others - the process of fusing all this data has not been efficient
enough to meet the needs of CGU’s decision makers. Therefore, it is natural to
change the focus from data fusion to knowledge fusion. As a consequence,
traditional syntactic methods must be augmented with techniques that represent
and reason with the semantics of databases. However, commonly used
approaches fail to deal with uncertainty, a dominant characteristic in corruption
prevention. This paper presents the use of Probabilistic OWL (PR-OWL) to
design and test a model that performs information fusion to detect possible
frauds in procurements involving Federal money. To design this model, a
recently developed tool for creating PR-OWL ontologies was used with support
from PR-OWL specialists and careful guidance from a fraud detection specialist
from CGU.
Keywords: Probabilistic Ontology, PR-OWL, Ontology, Procurement Fraud
Detection, Knowledge Fusion, MEBN, UnBBayes.
1 Introduction
A primary responsibility of the Brazilian Office of the Comptroller General (CGU) is
to prevent and detect government corruption. To carry out this mission, CGU must
gather information from a variety of sources and combine it to evaluate whether
4 R. Carvalho, K. Laskey, P. Costa, M. Ladeira, L. Santos, and S. Matsumoto
further action, such as an investigation, is required. One of the most difficult
challenges is the information explosion. Auditors must fuse vast quantities of
information from a variety of sources in a way that highlights its relevance to decision
makers and helps them focus their efforts on the most critical cases. This is no trivial
duty. The Growing Acceleration Program (PAC) alone has a budget greater than 250
billion dollars with more than one thousand projects only on the state of Sao Paulo
(http://www.brasil.gov.br/pac/). All of these have to be audited and inspected by CGU
– and, in spite having only three thousand employees. Therefore, CGU must optimize
its processes in order to carry out its mission.
The Semantic Web (SW), like the document web that preceded it, is based on
radical notions of information sharing. These ideas [1] include: (i) the Anyone can say
Anything about Any topic (AAA) slogan; (ii) the open world assumption, in which
we assume there is always more information that could be known, and (iii) nonunique
naming, which appreciates the reality that different speakers on the Web might use
different names to define the same entity. In a fundamental departure from
assumptions of traditional information systems architectures, the Semantic Web is
intended to provide an environment in which information sharing can thrive and a
network effect of knowledge synergy is possible. But this style of information
gathering can generate a chaotic landscape rife with confusion, disagreement and
conflict.
We call an environment characterized by the above assumptions a Radical
Information Sharing (RIS) environment. The challenge facing SW architects is
therefore to avoid the natural chaos to which RIS environments are prone, and move
to a state characterized by information sharing, cooperation and collaboration.
According to [1], one solution to this challenge lies in modeling, and this is where
ontologies languages like Web Ontology Language (OWL) come in.
As it will be shown in Section 3, the domain of procurement fraud detection is a
RIS environment. However, uncertainty is ubiquitous to knowledge fusion.
Uncertainty is especially important to applications such as fraud detection, in which
perpetrators seek to conceal illicit intentions and activities, making crisp assertions
extremely hard and rare. In such environments, partial (not complete) or approximate
(not exact) information is more the rule than the exception.
Bayesian networks (BNs) have been widely applied to draw inferences to
information and knowledge fusion in the presence of uncertainty. However, according
to [2] BNs are not expressive enough for many real-world applications. More
specifically, BNs assume a simple attribute-value representation – that is, each
problem instance involves reasoning about the same fixed number of attributes, with
only the evidence values changing from problem instance to problem instance.
Complex problems on the scale of the semantic web often involve intricate
relationships among many variables, and the limited representational power of BNs is
insufficient for building useful, detailed models.
Multi-Entity Bayesian Network (MEBN) logic can represent and reason with
uncertainty about any propositions that can be expressed in first-order logic [3].
Probabilistic OWL (PR-OWL) uses MEBN’s strengths to provide a framework for
building probabilistic ontologies (PO), a major step towards semantically aware,
probabilistic knowledge fusion systems [4]. This paper uses PR-OWL to design and
Prob. Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil 5
test a model for fusing information to detect possible frauds in procurements
involving Federal funds.
The paper is organized as follows. Section 2 introduces Multi-Entity Bayesian
Networks (MEBN), an expressive Bayesian logic, and PR-OWL, an extension of the
OWL language that can represent probabilistic ontologies having MEBN as its
underlying logic. Section 3 presents a case study from CGU to demonstrate the power
of PR-OWL ontologies for knowledge representation and fusion. Finally, Section 4
presents some concluding remarks.
2 MEBN and PR-OWL
Multi-Entity Bayesian Networks (MEBN) [5 and 6] extend BNs (BN) to achieve first-
order expressive power. MEBN represents knowledge as a collection of MEBN
Fragments (MFrags), which are organized into MEBN Theories (MTheories).
An MFrag contains random variables (RVs) and a fragment graph representing
dependencies among these RVs. An MFrag is a template for a fragment of a Bayesian
network. It is instantiated by binding its arguments to domain entity identifiers to
create instances of its RVs. There are three kinds of RV: context, resident and input.
Context RVs represent conditions that must be satisfied for the distributions
represented in the MFrag to apply. Input nodes represent RVs that may influence the
distributions defined in the MFrag, but whose distributions are defined in other
MFrags. Distributions for resident RV instances are defined in the MFrag.
Distributions for resident RVs are defined by specifying local distributions
conditioned on the values of the instances of their parents in the fragment graph.
A set of MFrags represents a joint distribution over instances of its random
variables. MEBN provides a compact way to represent repeated structure in a BN. An
important advantage of MEBN is that there is no fixed limit on the number of RV
instances, and the random variable instances are dynamically instantiated as needed.
An MTheory is a set of MFrags that satisfies conditions of consistency ensuring
the existence of a unique joint probability distribution over its random variable
instances.
To apply an MTheory to reason about particular scenarios, one needs to provide
the system with specific information about the individual entity instances involved in
the scenario. On receipt of this information, Bayesian inference can be used both to
answer specific questions of interest (e.g., how likely is it that a particular
procurement is being directed to a specific enterprise?) and to refine the MTheory
(e.g., each new tactical situation includes additional statistical data about the
likelihood of a given attack for that set of circumstances). Bayesian inference is used
to perform both problem specific inference and learning in a sound, logically coherent
manner (for more details see [6 and 7]).
State-of-the-art systems are increasingly adopting ontologies as a means to ensure
formal semantic support for knowledge sharing [8, 9, 10, 11, 12, and 13].
Representing and reasoning with uncertainty is becoming recognized as an essential
capability in many domains. A common error is to provide support for uncertainty
representation by just annotating ontologies with numerical probabilities. This
6 R. Carvalho, K. Laskey, P. Costa, M. Ladeira, L. Santos, and S. Matsumoto
approach leads to brittleness, as too much information is lost due to the lack of a
representational scheme that can capture structural nuances of the probabilistic
information. More expressive representation formalisms are needed [4].
Fig. 1. PR-OWL main concepts.
Probabilistic Ontologies (PR-OWL) [14 and 15] was proposed as a more
expressive formalism for representing knowledge in domains characterized by
uncertainty. Figure 1 presents the main concepts needed to define an MTheory in PR-
OWL. In the diagram, the ellipses represent the general classes, while the arcs
represent the main relationships among the classes.
The procurement fraud detection probabilistic ontology was built in UnBBayes-
MEBN, a tool for building and reasoning with PR-OWL probabilistic ontologies.
UnBBayes-MEBN was the first software to implement PR-OWL/MEBN (see [16, 17,
18, 19] for more details). UnBBayes-MEBN supports Multi-Entity Bayesian Network
(MEBN) and enables creation and editing of Probabilistic Ontologies in PR-OWL
[18]. The MEBN/PR-OWL Graphical User Interface (GUI) [16] allows users to
define MFrags and make probabilistic queries. UnBBayes-MEBN also implements an
algorithm for generating a Situation Specific Bayesian Network (SSBN) [18, 19],
which is an ordinary BN created by instantiating instances of the MFrags to respond
to a probabilistic query. Once the SSBN is generated, the inference engine
(Reasoning) is called to process findings and update beliefs. UnBBayes-MEBN uses
the Protégé-OWL library to load and save PR-OWL files (IO) in a format compatible
with OWL. It supports first order logic context node evaluation (FOL), through the
use of the PowerLoom library. It also defines and implements a built-in mechanism
for typing and recursion. Finally, it permits the definition of dynamic conditional
probabilistic tables.
UnBBayes has proven to be a simple, yet powerful, tool for designing probabilistic
ontologies and for uncertain reasoning in complex situations such as procurement
fraud detection. It is straightforward to use and provides powerful features (e.g.
dynamic table) not available in systems (e.g., Quiddity) previously employed to
reason with PR-OWL/MEBN knowledge bases.
Prob. Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil 7
3 Procurement Fraud Detection
A major source of corruption is the procurement process. Although laws attempt to
ensure a competitive and fair process, perpetrators find ways to turn the process to
their advantage while appearing to be legitimate. This is why a specialist has
didactically structured the different kinds of procurement frauds CGU has dealt with
in past years.
These different fraud types are characterized by criteria, such as business owners
who work as a front for the company, use of accounting indices that are not common
practice, etc. Indicators have been established to help identify cases of each of these
fraud types. For instance, one principle that must be followed in public procurement is
that of competition. Every public procurement should establish minimum requisites
necessary to guarantee the execution of the contract in order to maximize the number
of participating bidders. Nevertheless, it is common to have a fake competition when
different bidders are, in fact, owned by the same person. This is usually done by
having someone as a front for the enterprise, which is often someone with little or no
education.
The ultimate goal of this case study is to structure the specialist knowledge in a
way that an automated system can reason with the evidence in a manner similar to the
specialist. Such an automated system is intended to support specialists and to help
train new specialists, but not to replace them. Initially, a few simple criteria were
selected as a proof of concept. Nevertheless, it is shown that the model can be
incrementally updated to incorporate new criteria. In this process, it becomes clear
that a number of different sources must be consulted to come up with the necessary
indicators to create new and useful knowledge for decision makers about the
procurements.
Fig. 2. Procurement fraud detection overview.
Figure 2 presents an overview of the procurement fraud detection process. The data
for our case study represent several requests for proposal and auctions that are issued
8 R. Carvalho, K. Laskey, P. Costa, M. Ladeira, L. Santos, and S. Matsumoto
by the Federal, State and Municipal Offices (Public Notices – Data). As the focus of
the work is in representing the specialist knowledge and reasoning through
probabilistic ontologies and not in the collection of information, the idea is that the
analysts that work at CGU, already making audits and inspections, accomplish the
collection of information through questionnaires that can specifically be created for
the collecting of indicators for the selected criteria (Information Gathering). These
questionnaires can be created using a system that is already in production at CGU.
Once they are answered the necessary information is going to be available (DB –
Information). Hence, UnBBayes, using the probabilistic ontology designed by experts
(Design – UnBBayes), will be able to collect these millions of items of information
and transform them into dozens or hundreds of items of knowledge, through logic and
probabilistic inference, e.g. procurement announcements, contracts, reports, etc - a
huge amount of data - are analyzed allowing the gathering of relevant relations and
properties - a large amount of information - which in turn are used to draw some
conclusions about possible irregularities - a smaller number of items of knowledge
(Inference – Knowledge). This knowledge can be filtered so that only the
procurements that show a probability higher than a threshold, e.g. 20%, are
automatically forwarded to the responsible department along with the inferences
about potential fraud and the supporting evidence (Report for Decision Makers).
The criteria selected by the specialist were the use of accounting indices and the
demand of experience in just one contract. There are four common types of indices
that are usually used as requirements in procurements (ILC, ILG, ISG, and IE). Any
other type could indicate a made-up index specifically designed to direct the
procurement to some specific company. The greater the numbers of uncommon
accounting indices used by the procurement the more suspicious it is, i.e. the higher
the chance of having fraud. In addition, a procurement specifies a minimum value for
these accounting indices. The minimum value that is usually required is 1.0. The
higher this minimum value, the more the competition is narrowed, and therefore the
higher the chance the procurement is being directed to some company.
Fig. 3. ProcurementRequirement MFrag.
The other criterion, demanding proof of experience in only one contract, is suspect
because in almost every case, the experience is not gained only by a particular
contract, but also by doing it over and over again in different contracts. It does not
matter if you have built 1,000 ft2 of wall in just one contract or 100 ft2 in 10 different
contracts. The experience gained will be basically the same.
The procurement fraud detection model was developed as a probabilistic ontology
(using PR-OWL) to define its semantics and uncertain characteristics. The MTheory
created for the model, using UnBBayes-MEBN, was divided into three different
MFrags.
Prob. Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil 9
The first, Figure 3, presents the criteria required from a company to participate in
the procurement, containing information about the type of accounting index (ILC,
ILG, ISG, IE, and Other) and the minimum value for it (between 0 and 1, between 1
and 2, between 2 and 3, and greater than 3). This MFrag also contains information
about where a specific index is used (which procurement), and if the procurement
demands experience in only one contract.
Fig. 4. DirectingProcurementByIndexes MFrag.
The second, Figure 4, represents whether procurement is being directed to a
specific company by the use of unusual accounting indices. As explained before, this
analysis is based on the type of the index and the minimum value it requires. This
evaluation takes into consideration every index used in a specific procurement, hence
it is dynamic.
The last MFrag, Figure 5, represents the overall possibility that procurement is
being directed to a specific company based on the result of its being directed by the
use of unusual indices and by the requirement of experience in only one contract, as
explained before.
Fig. 5. DirectingProcurement MFrag.
To test the model, two scenarios, that represent the two groups of suspect and non
suspect procurements, were chosen from a set of real cases, as shown:
• Suspect procurement (proc1):
o ind1 = ILC >= 2.0;
o ind2 = ILG >= 1.5;
o ind3 = Other >= 3.0.
o It demands experience in only one contract.
• Non suspect procurement (proc2):
o ind4 = IE >= 1.0;
10 R. Carvalho, K. Laskey, P. Costa, M. Ladeira, L. Santos, and S. Matsumoto
o ind5 = ILG >= 1.0;
o ind6 = ILC >= 1.0;
o It does not demand experience in only one contract.
The information above was introduced in our model as known entities and
findings. After that we queried the system to give us information about the node
IsProcurementDirected(proc) for both proc1 and proc2. UnBBayes-MEBN than
executed the SSBN algorithm and generated the same node structure as shown in
Figure 6, because both procurements have three accounting indices and information
about the demanding experience in only one contract. However, as expected, the
parameters and findings are different giving different results to the query, as shown
below:
• Non suspect procurement:
o 0.01% that the procurement was directed to a specific company by
using accounting indices;
o 0.10% that the procurement was directed to a specific company.
• Suspect procurement:
o 55.00% that the procurement was directed to a specific company by
using accounting indices;
o 29.77%, when the information about demanding experience in only
one contract was omitted, and 72.00%, when it was given, that the
procurement was directed to a specific company.
Fig. 6. Generated SSBN for query IsProcurementDirected(proc1).
The specialist analyzed and agreed with the knowledge generated by the
probabilistic ontology reasoned developed using PR-OWL/MEBN in UnBBayes. He
stated that the probabilities represent, semantically (i.e. high, medium, and low
chance), what he would think when analyzing the same entities and findings.
Although the SSBNs generated for this proof of concept present the same structure,
it is common to have a different one as the context varies from procurement to
Prob. Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil 11
procurement. For instance, we have come across several procurements that have all
four common indices and some other different ones. In this case, if there are two
additional indices (ind5 and ind6), then the resulting SSBN would have two more
copies for nodes IndexType(index) andIndexMinValue(index). This would make the
use of BN not applicable. The ability to make multiple copies of nodes based on a
context is only available in a more expressive formalism, as MEBN.
Fig. 7. EnterpriseBusinessNetwork MFrag.
An additional capability not available with BN is to specify constraints on
applicability of knowledge. Such constraints can only be implemented in a more
expressive language. As we are dealing with BN formalism it is only natural to think
of a formalism that extends BN. MEBN, as a Bayesian first-order logic, makes it
possible to define these constraints using FOL.
Figure 7 presents the constraints (context nodes) necessary to model the fraud
detection scenarios considered here. In this MFrag, the criterion is to identify if there
is a suspicious business relationship between enterprises entA and entB. The more
cases where enterprise B wins a procurement that the basic project was developed by
enterprise A, the higher the chance they have some kind of personal business
relationship, which means that it is more likely that enterprise B is developing the
basic projects in such a way that will favor enterprise A, inhibiting the desired
competition.
Fig. 8. OwnerFront MFrag.
Since the designed model is restricted to just two criteria, the team started to think
about other criteria that could be incorporated and tested further. Figure 8 presents the
suggested MFrag for detecting owners that act as a front to the real owner of the
company (the person who really has the power to make decisions and that gets all the
money), by looking up their socio-economic attributes and checking the size of the
12 R. Carvalho, K. Laskey, P. Costa, M. Ladeira, L. Santos, and S. Matsumoto
company. In other words, if a company is highly profitable, yet has an owner with
little education, low income, no car, no house, etc, then the company is probably a
front.
Fig. 9. Knowledge fusion from different Government Offices DBs.
From the criteria presented and modeled in this Section, we can clearly see the
need for a principled way of dealing with uncertainty. But what is the role of
Semantic Web in this domain? Well, it is easy to see that our domain of fraud
detection is a RIS environment. The data CGU has available does not come only from
its audits and inspections. In fact, much complementary information can be retrieved
from other Federal Agencies, including Federal Revenue Agency, Federal Police, and
others. Imagine we have information about the enterprise that won the procurement,
and we want to know information about its owners, such as their personal data and
annual income. This type of information is not available at CGU’s Data Base (DB),
but must be retrieved from the Federal Revenue Agency’s DB. Once the information
about the owners is available, it might be useful to check their criminal history. For
that (see Figure 9), information from the Federal Police must be used. In this example,
we have different sources saying different things about the same person: thus, the
AAA slogan applies. Moreover, there might be other Agencies with crucial
information related to our person of interest; in other words, we are operating in an
open world. Finally, to make this sharing and integration process possible, we have to
make sure we are talking about the same person, who may (especially in case of
fraud) be known by different names in different contexts.
5 Conclusion
The problem that CGU and many other Agencies have faced of processing all the
available data into useful knowledge is starting to be solved with the use of
Prob. Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil 13
probabilistic ontologies, as the procurement fraud detection model showed. Besides
fusing the information available, the designed model was able to represent the
specialist knowledge for the two real cases we evaluated. UnBBayes reasoning given
the evidence and using the designed model were accurate both in suspicious and non
suspicious scenarios. These results are encouraging, suggesting that a fuller
development of our proof of concept system is promising.
In addition, it is fairly easy to introduce new criteria and indicators in the model in
an incremental way. Thus, new rules for identifying fraud can be added without
rework. After a new rule is incorporated into the model, a set of new tests can be
added to the previous one with the objective of always validating the new model
proposed, without doing everything from scratch.
Furthermore, the use of this formalism through UnBBayes allows advantages such
as impartiality in the judgment of irregularities in procurements (given the same
conditions the system will always deliver the same result), scalability (capacity to
analyze thousands of procurements in a short time when compared to human
capacity) and a joint analysis of large volumes of indicators (the higher the number of
indicators to examine jointly the more difficult it is for the specialist analysis to be
objective and consistent).
As a next step, CGU is choosing new criteria to be incorporated into the designed
probabilistic ontology. This next set of criteria will require information from different
Brazilian Agencies’ databases. Therefore, the semantic power of ontologies with the
uncertainty handling capability of PR-OWL will be extremely useful for fusing
information from multiple databases.
Acknowledgments. Rommel Carvalho gratefully acknowledges full support from the
Brazilian Office of the Comptroller General (CGU) for the research reported in this
paper, and its employees involved in this research, especially Mário Vinícius
Claussen Spinelli, the domain expert.
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