PR-OWL – a language for defining probabilistic ontologies

Across a wide range of domains, there is an urgent need for a wellfounded approach to incorporating uncertain and incomplete knowledge into formal domain ontologies. Although this subject is receiving increasing attention from ontology researchers, there is as yet no broad consensus on the definition of a probabilistic ontology and on the most suitable approach to extending current ontology languages to support uncertainty. This paper presents two contributions to developing a coherent framework for probabilistic ontologies: (1) a formal definition of a probabilistic ontology, and (2) an extension of the OWL Web Ontology Language that is consistent with our formal definition. This extension, PR-OWL, is based on Multi-Entity Bayesian Networks (MEBN), a first-order Bayesian logic that unifies Bayesian probability with First-Order Logic. As such, PR-OWL combines the full representation power of OWL with the flexibility and inferential power of Bayesian logic.

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, PR-OWL, will allow legacy ontologies to interoperate with newly developed probabilistic ontologies. PR-OWL 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, PR-OWL 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.

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.

2009

2006

Across a wide range of domains, there is an urgent need for a wellfounded approach to incorporating uncertain and incomplete knowledge into formal domain ontologies. Although this subject is receiving increasing attention from ontology researchers, there is as yet no broad consensus on the definition of a probabilistic ontology and on the most suitable approach to extending current ontology languages to support uncertainty. This paper presents two contributions to developing a coherent framework for probabilistic ontologies: (1) a formal definition of a probabilistic ontology, and (2) an extension of the OWL Web Ontology Language that is consistent with our formal definition. This extension, PROWL , is based on Multi-Entity Bayesian Networks (MEBN), a first-order Bayesian logic that unifies Bayesian probability with First-Order Logic. As such, PROWL combines the full representation power of OWL with the flexibility and inferential power of Bayesian logic.

2015

Probabilistic OWL (PR-OWL) improves the Web Ontology Language (OWL) with the ability to treat uncertainty using Multi-Entity Bayesian Networks (MEBN). PROWL 2 presents a better integration with OWL and its underlying logic, allowing the creation of ontologies with probabilistic and deterministic parts. However, there are scalability problems since PROWL 2 is built upon OWL 2 DL which is a version of OWL based on description logic SROIQ(D) and with high complexity. To address this issue, this paper proposes PROWL 2 RL, a scalable version of PROWL based on OWL 2 RL profile and triplestores (databases based on RDF triples). OWL 2 RL allows reasoning in polynomial time for the main reasoning tasks. This paper also presents First-Order expressions accepted by this new language and analyzes its expressive power. A comparison with the previous language presents which kinds of problems are more suitable for each version of PROWL .

The main idea behind the Semantic Web is the representation of knowledge in an explicit and formal way. This is done using ontology representation languages as OWL, which is based on Description Logics and other logic formalisms. One of the main objectives with this kind of knowledge representation is that it can then be used for reasoning. But the way reasoning is done in the Semantic Web technology is very strict, defining only a right and wrong view of the world. The real world is uncertain and humans have learned how to deal with this crucial aspect. In this paper, we present an approach to reasoning with uncertainty information in the Semantic Web. We have applied Markov Logic, which is able to reason with uncertainty information, to several Semantic Web ontologies, showing that it can be used in several applications. We also describe the main challenges for reasoning with uncertainty in the Semantic Web.

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).

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.

Iswc, 2006

Service Oriented Architecture (SOA) is a key technology to support interoperability among data and processing resources. Semantic interoperability requires mapping between vocabularies of independently developed resources, a task fraught with uncertainty. Probabilistic ontologies enable representation of knowledge in domains characterized by uncertainty. As such, they promise to improve the quality of service descriptions, enable more thorough analysis of service composition opportunities, and provide a theoretically sound methodology for semantic mapping under uncertainty. This paper defines probabilistic ontologies, discusses their application to SOA, and presents a conceptual scheme for using a federation of ontologies (with both common and probabilistic ontologies) as a semantic mapping tool for service oriented information exchange systems with different levels of service descriptions (including legacy and probabilistic enabled descriptions). 1 Semantic Interoperability in the Semantic Web and Service Oriented Architecture Frameworks A fundamental tenet of the Semantic Web (SW) vision is that adding semantics to web resources can spark a paradigm shift from information-based data exchange to knowledge-based data-exchange. HTML syntax hard-codes a limited single-purpose set of semantic categories. In contrast, the Semantic Web envisions resources annotated with well-defined, explicitly represented semantics that provides the basis for rich description and reasoning. Explicit semantics is essential for appropriate processing of syntactically identical but semantically different terms (e.g., "Washington" the President, the city, or the football team). Ontologies, or shared repositories of precisely defined concepts expressed in standardized languages, are a vital tool for enabling semantic interoperability among web resources. Thus, ontologies are a means for transforming the current "Web of shared data" into a "Web of shared knowledge."