PR-OWL 2 Case Study: A Maritime Domain Probabilistic Ontology

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 . 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.

2005

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 propose to extend OWL, the ontology language recommended by the World Wide Web Consortium (W3C), to provide the ability to express probabilistic knowledge. The new language, PROWL , will allow legacy ontologies to interoperate with newly developed probabilistic ontologies. PROWL will move 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, PROWL will serve as a supporting tool for many applications that can benefit from probabilistic inference within an ontology language, thus representing an important step toward the W3C's vision for the Semantic Web.

One of the major weaknesses of current research on the Semantic Web (SW) is the lack of proper means to represent and reason with uncertainty. A number of recent efforts from the SW community, the W3C, and others have recently emerged to address this gap. Such efforts have the positive side effect of bringing together two fields of research that have been apart for historical reasons, the artificial intelligence and the SW communities. One example of the potential research gains of this convergence is the current development of Probabilistic OWL (PR-OWL), an extension of the OWL Web Ontology Language that provides a framework to build probabilistic ontologies, thus enabling proper representation and reasoning with uncertainty within the SW context. PR-OWL is based on Multi-Entity Bayesian Networks (MEBN), a first-order probabilistic logic that combines the representational power of first-order logic (FOL) and Bayesian Networks (BN). However, PR-OWL and MEBN are still in development, lacking a software tool that implements their underlying concepts. The development of UnBBayes-MEBN, an open source, Java-based application that is currently in alpha phase (public release March 08), addresses this gap by providing both a GUI for building probabilistic ontologies and a reasoner based on the PR-OWL/MEBN framework. This work focuses on the major challenges of UnBBayes-MEBN implementation, describes the features already implemented, and provides an overview of the major algorithms, mainly the one used for building a Situation Specific Bayesian Network (SSBN) from a MEBN Theory.

IGI Global eBooks, 2018

During the past years, ontologies are widely used for representing knowledge of complex domains. Despite that the ontologies (classical ontologies) have become standard for representing knowledge; however, they are not able to represent and reason with uncertainty which is one of the characteristics of the world that must be handled. Probabilistic Ontologies have come to remedy this defect. This paper is part of this framework in which the authors have proposed a new method of probabilistic ontology construction, named Prob-Ont, by integrating uncertainty to elements of OWL ontology (especially to instances and/or relations). As a case study, the authors have constructed a probabilistic ontology for the domain of scientific documentation system (dblp).

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 General Comptroller (CGU).

2013

2013

The use of ontologies is on the rise, as they facilitate interoperability and provide support for automation. Today, ontologies are popular for research in areas such as the Semantic Web, knowledge engineering, artificial intelligence and knowledge management. However, many real world problems in these disciplines are burdened by incomplete information and other sources of uncertainty which traditional ontologies cannot represent. Therefore, a means to incorporate uncertainty is a necessity. Probabilistic ontologies extend current ontology formalisms to provide support for representing and reasoning with uncertainty. Representation of uncertainty in real-world problems requires probabilistic ontologies, which integrate the inferential reasoning power of probabilistic representations with the first-order expressivity of ontologies. This paper introduces a systematic approach to probabilistic ontology development through a reference architecture which captures the evolution of a traditional ontology into a probabilistic ontology implementation for real-world problems. The Reference Architecture for Probabilistic Ontology Development catalogues and defines the processes and artifacts necessary for the development, implementation and evaluation of explicit, logical and defensible probabilistic ontologies developed for knowledgesharing and reuse in a given domain.