PR-OWL: A Bayesian Ontology Language for the Semantic Web

Costa, Paulo C. G.; Laskey, Kathryn Blackmond

doi:10.1007/978-3-540-89765-1_6

Cited by 85 publications

(57 citation statements)

References 6 publications

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“…Some people have argued that uncertainty should be explicitly represented in an ontology because of the inherent uncertainty in data (Pool et al, 2005;da Costa et al, 2005;Laskey et al, 2007). While we believe that it is essential to model the uncertainty in data, we don't believe actual probability values should be in the ontology 4 .…”

Section: Data and Ontologiesmentioning

confidence: 93%

Semantic Science: Ontologies, Data and Probabilistic Theories

Poole

Smyth²,

Sharma³

2008

Lecture Notes in Computer Science

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Abstract. This chapter overviews work on semantic science. The idea is that, using rich ontologies, both observational data and theories that make (probabilistic) predictions on data are published for the purposes of improving or comparing the theories, and for making predictions in new cases. This paper concentrates on issues and progress in having machine accessible scientific theories that can be used in this way. This paper presents the grand vision, issues that have arisen in building such systems for the geological domain (minerals exploration and geohazards), and sketches the formal foundations that underlie this vision. The aim is to get to the stage where: any new scientific theory can be tested on all available data; any new data can be used to evaluate all existing theories that make predictions on that data; and when someone has a new case they can use the best theories that make predictions on that case.

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Section: Data and Ontologiesmentioning

confidence: 93%

Semantic Science: Ontologies, Data and Probabilistic Theories

Poole

Smyth²,

Sharma³

2008

Lecture Notes in Computer Science

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show abstract

“…This means encoding the classes, attributes, relationships and rules in the chosen language. For our case study, the mapping is to PR-OWL (Carvalho, Laskey & Costa, 2013;Costa, Laskey & Laskey, 2008), but other semantically rich uncertainty representation languages could also be used (e.g., Cozman & Mauá, 2015).…”

Section: Uncertainty Modeling Process For Semantic Technologymentioning

confidence: 99%

“…In recognition of this need, the past decade has seen a significant increase in formalisms that integrate uncertainty representation into ontology languages. This has given birth to several new languages such as: PR-OWL (Costa, 2005;Costa, Laskey & Laskey, 2005;Costa, Laskey & Laskey, 2008;Carvalho, 2011;Carvalho, Laskey & Costa, 2013), OntoBayes (Yang & Calmet, 2005), BayesOWL (Ding, Peng & Pan, 2006), P-CLASSIC (Koller, Levy & Pfeffer, 1997) and probabilistic extensions of SHIF (D) and SHOIN(D) (Lukasiewicz, 2008).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty modeling process for semantic technology

Carvalho

Laskey

Costa

2016

PeerJ Computer Science

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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 can be used 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.

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“…The uncertainty value in this context denotes a membership function µ F (x) which specifies the degree to which an object x belongs to a fuzzy class F . Probabilistic adaptations of OWL-DL include Bayes OWL [11] and PR-OWL [12]. However, both of these formalisms do not fully reflect the properties of the problems we are dealing with in the fusion scenario.…”

Section: Related Workmentioning

confidence: 99%

Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies

Nikolov

Uren

Motta

et al. 2008

Lecture Notes in Computer Science

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Abstract. Automated ontology population using information extraction algorithms can produce inconsistent knowledge bases. Confidence values assigned by the extraction algorithms may serve as evidence helping to repair produced inconsistencies. The Dempster-Shafer theory of evidence is a formalism, which allows appropriate interpretation of extractors' confidence values. The paper presents an algorithm for translating the subontologies containing conflicts into belief propagation networks and repairing conflicts based on the Dempster-Shafer plausibility.

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PR-OWL: A Bayesian Ontology Language for the Semantic Web

Cited by 85 publications

References 6 publications

Semantic Science: Ontologies, Data and Probabilistic Theories

Semantic Science: Ontologies, Data and Probabilistic Theories

Uncertainty modeling process for semantic technology

Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies

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