Rommel N. Carvalho

PR-OWL 2.0 - bridging the gap to OWL semantics

The past few years have witnessed an increasingly mature body of research on the Semantic Web, with new standards being developed and more complex use cases being proposed and explored. As complexity increases in SW applications, so does the need for principled means to cope with uncertainty inherent to real world SW applications. Not surprisingly, several approaches addressing uncertainty representation and reasoning on the Semantic Web have emerged [3, 4, 6, 7, 10, 11, 13, 14]. For example, PR-OWL [3] provides OWL constructs for representing Multi-Entity Bayesian Network (MEBN) [8] theories. This paper reviews some shortcomings of PR-OWL 1 [2] and describes how they will be addressed in PR-OWL 2. A method is presented for mapping back and forth from triples into random variables (RV). The method applies to triples representing both predicates and functions. A complex example is given for mapping an n-ary relation using the proposed schematic.

PROGNOS: Predictive situational awareness with probabilistic ontologies

Information in the battlefield comes from reports from diverse sources, in distinct syntax, and with different meanings. There are many kinds of uncertainty involved in this process, e.g., noise in sensors, incorrect, incomplete, or deceptive human intelligence, and others, which makes it essential to have a coherent, consistent, and principled means to represent such phenomena among the systems performing Predictive Situation Awareness (PSAW). PROGNOS is a PSAW system being developed to work within the operational context such as U.S. Navys FORCENet. It employs probabilistic ontologies in a distributed system architecture as a means to provide semantic interoperability within an intrinsically complex and uncertain environment. This paper explores our current status in developing the system while addressing the major research challenges for making an effective PSAW system to support maritime operations.

UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web

The same assumptions that were essential in the document web are still applied for the Semantic Web (SW). They are radical notions of information sharing, which include [Allemang & Hendler, 2008]: (i) the Anyone can say Anything about Any topic (AAA) slogan; (ii) the open world assumption, i.e. there might exist more information out there that we are not aware of, and (iii) nonunique naming, meaning that different people can assign different names to the same concept. However, the Semantic Web differs from its predecessors in the sense that it intends to provide an environment not only for allowing information sharing but also for making it possible to have the effect of knowledge synergy. Nevertheless, this can lead to a chaotic scenario with disagreements and conflicts. 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 [Allemang & Hendler, 2008], one solution to this challenge lies in modeling. Modeling is a simplified abstraction of some real world phenomenon, which, amongst other things, allows the organizing of information for the community use. Modeling supports information sharing in three ways: it provides a means for human communication, it provides a way for explaining conclusions, and it provides the managing of different viewpoints. There is an immense variety of modeling approaches. In this chapter we will go over a few of these approaches, showing how they can be used and their main limitations related to achieving the full potential of the Semantic Web. First we will show how to apply Unified Modeling Language (UML) [Rumbaugh et al., 1998] and Entity/Relationship (ER) [Chen, 1976] diagrams for modeling. Then we will present Knowledge Representation and Reasoning (KR&R) [Brachman & Levesque, 2004] and describe how KR&R overcomes some of the limitations of UML and ER. Finally, we present Ontology and the Semantic Web [Berners-Lee, 1999] and discuss how it differs from and moves beyond the previous approaches.

Probabilistic ontology and knowledge fusion for procurement fraud detection in Brazil

To cope with societys 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 CGUs 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.

A GUI Tool for Plausible Reasoning in the Semantic Web using MEBN

As the work with semantics and services grows more ambitious in the semantic Web community, there is an increasing appreciation on the need for principled approaches for representing and reasoning under uncertainty. Reacting to this trend, the World Wide Web Consortium (W3C) has created the Uncertainty Reasoning for the World Wide Web Incubator Group (URW3-XG) to better define the challenge of reasoning with and representing uncertain information available through the World Wide Web and related WWW technologies. In according to the URW3-XG effort this paper presents the implementation of a graphical user interface for building probabilistic ontologies, an application programming interface for saving and loading these ontologies and a proposal to specify formulas for creating conditional probabilistic tables dynamically. The language used for building probabilistic ontologies is probabilistic OWL (Pr-OWL), an extension for OWL based on multi-entity Bayesian network (MEBN).

Applying clustering and AHP methods for evaluating suspect healthcare claims

Abstract This paper seeks to present a model for the analysis of suspicious claims data from healthcare providers with the use of different clustering algorithms, and the application of the AHP multicriteria method for prioritizing the identified suspect entities for subsequent auditing. We begin with a brief overview of related works that have covered the application of the aforementioned techniques for investigating suspicious entities in the context of internal auditing and healthcare. After presenting the steps for the construction of our own model, we discuss our results. We determine that the application of clustering algorithms to our initial variables resulted in the automatic detection of almost all entities initially classified as suspect. Our AHP model then provided us with rational criteria for effectively and objectively ranking these entities for further investigation

Uncertainty modeling process for semantic technology

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model is intended to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

PR-OWL – a language for defining probabilistic ontologies

Abstract Recent 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 to cope with uncertainty. Several approaches to uncertainty representation and reasoning in the SW have emerged. Among these is Probabilistic Web Ontology Language (PR-OWL), which provides a means of representing uncertainty in ontologies expressed in Web Ontology Language (OWL). PR-OWL allows values of random variables to range over OWL datatypes, following an 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.

Deep Learning Anomaly Detection as Support Fraud Investigation in Brazilian Exports and Anti-Money Laundering

Normally exports of goods and products are transactions encouraged by the governments of countries. Typically these incentives are promoted by tax exemptions or lower tax collections. However, exports fraud may occur with objectives not related to tax evasion, for example money laundering. This article presents the results obtained in implementing the unsupervised Deep Learning model to classify Brazilian exporters regarding the possibility of committing fraud in exports. Assuming that the vast majority of exporters have explanatory features of their export volume which interrelate in a standard way, we used the AutoEncoder to detect anomalous situations with regards to the data pattern. The databases used in this work come from exports of goods and products that occurred in Brazil in 2014, provided by the Secretariat of Federal Revenue of Brazil. From attributes that characterize export companies, the model was able to detect anomalies in at least twenty exporters.

Analyzing Suspicious Medical Visit Claims from Individual Healthcare Service Providers Using K-Means Clustering

This study has as its main objective the analysis of healthcare claims data from individual providers, such as independent doctors and allied health professionals, with the purpose of finding excessive billing of medical visitation procedures. We present a discussion of the main difficulties in preventing against abusive claims, and with the use of the CRISP-DM method and the k-means clustering algorithm, propose a model for assessing the behavior of providers engaged in this sort of practice. We conclude that the clustering algorithm was able to provide a more efficient, objective, and reproducible framework for identifying outliers, which could be used for future investigations in similar datasets.