Paulo C. G. Costa

PR-OWL: a Bayesian ontology language for the semantic web

This paper addresses a major weakness of current technologies for the Semantic Web, namely the lack of a principled means to represent and reason about uncertainty. This not only hinders the realization of the original vision for the Semantic Web, but also creates a barrier to the development of new, powerful features for general knowledge applications that require proper treatment of uncertain phenomena. We present PR-OWL, a probabilistic extension to the OWL web ontology language that allows legacy ontologies to interoperate with newly developed probabilistic ontologies. PR-OWL moves beyond the current limitations of deterministic classical logic to a full first-order probabilistic logic. By providing a principled means of modeling uncertainty in ontologies, PR-OWL can be seen as a supporting tool for many applications that can benefit from probabilistic inference within an ontology language, thus representing an important step toward the W3Cs vision for the Semantic Web. In order to fully present the concepts behind PR-OWL, we also cover Multi-Entity Bayesian Networks (MEBN), the Bayesian first-order logic supporting the language, and UnBBayes-MEBN, an open source GUI and reasoner that implements PR-OWL concepts. Finally, a use case of PR-OWL probabilistic ontologies is illustrated here in order to provide a grasp of the potential of the framework.

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).

Dynamic decision making: a comparison of approaches

This paper is concerned with a specific type of problem, namely dynamic decisions, for which most techniques fail to provide adequate solutions. Here, we present two of the most promising optimization techniques, partially observable Markov decision processes (POMDP) and dynamic decision networks (DDN), while arguing which is the most suitable for this problem domain. Copyright

Integrity and authenticity of ADS-B broadcasts

We propose a novel approach to provide authenticity and integrity of Automatic Dependent Surveillance-Broadcast (ADS-B) messages. We employ a key-management schema for authentication and rely on a keyed-hashed message authentication code (HMAC) for integrity. Our approach avoids scalability and compatibility issues, as we neither change the packet format nor its size.

The URREF ontology for semantic wide area motion imagery exploitation

Current advances operational information fusion systems (IFSs) require common semantic ontologies for collection, storage, and access to multi intelligence information. One example is the connections between physics-based (e.g. video) and text-based (e.g. reports) describing the same situation. Situation, user, and mission awareness are enabled through a common ontology. In this paper, we utilize the uncertainty representation and reasoning evaluation framework (URREF) ontology as a basis for describing wide-area motion imagery (WAMI) analysis to determine uncertainty attributes. As part of the Evaluation of Technologies for Uncertainty Representation Working Group (ETURWG), both the URREF and a WAMI challenge problem are available for research purposes from which we provide an exemplar schema to link physics-based and text-based uncertainty representations to explore a common uncertainty demonstration.

High-level fusion: Issues in developing a formal theory

Network-centric operations demand an increasingly sophisticated level of interoperation and information fusion for an escalating number and throughput of sensors and human processes. The resulting complexity of the systems being developed to face this environment render lower level fusion techniques alone simply insufficient to ensure interoperability, as they fail to consider subtle, but critical, aspects inherent in knowledge interchange. A fundamental mathematical theory of high-level information fusion is needed to address (1) the representation of semantics and pragmatics, (2) the mathematical framework supporting its algorithmic and computing processes, and (3) scalability of products such as common and user-defined operational pictures. We argue that there is no silver bullet for addressing these elements, and therefore any successful approach to the problem of high-level fusion must be systemic. In this paper, we propose the development of mathematical foundations that systemically address this problem from a decision theoretic perspective, and might seed the development of such fundamental theory. As a case study illustrating these techniques we present our current development of PROGNOS, a HLF system focused on the maritime domain.