Laécio L. Santos

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).

Entity Extraction within Plain-Text Collections WISE 2013 Challenge - T1: Entity Linking Track

The WISE 2013 conference proposed a challenge (T1 Track) in which teams must label entities within plain texts based on Wikilinks dataset which comprises 40 million mentions over 3 million existed entities. This paper describe a straightforward two-fold unsupervised strategy to extract and tag entities, aiming to achieve accurate results in the identification of proper nouns and concrete concepts, regardless the domain. The proposed solution is based on a pipeline of text processing modules that includes a lexical parser. The solution labelled 8824 texts, and the results achieved satisfying precision measures.

UMP-ST Plug-in: Documenting, Maintaining and Evolving Probabilistic Ontologies Using UnBBayes Framework

Several approaches have been proposed for dealing with uncertainty in the Semantic Web SW. Although probabilistic ontologies PO is one of the most promising approach to model uncertainty in ontologies, no support has been offered to ontological engineers on how to create this more complex type of ontologies. This task has proven to be extremely difficult and hard, which motivated the creation of the Uncertainty Modeling Process for Semantic Technologies UMP-ST, a process that guides users in modeling POs. This paper presents the UMP-ST plug-in, a tool that implements this process and shows how the plug-in, implemented in UnBBayes Framework, overcomes the main problems on modeling probabilistic ontologies: the complexity in creating; the difficulty in maintaining and evolving; and the lack of a centralized tool for documenting these ontologies. The probabilistic ontology for Procurement Fraud Detection and Prevention in Brazil is used to show how the UMP-ST plug-in overcomes these problems. This probabilistic ontology is a proof-of-concept use case created as part of a research project at the Brazilian Office of the Comptroller General CGU. A short version of this paper was presented on the URSW 2013i¾?[3].