An Individual-centric Probabilistic Extension for OWL: Modelling the Uncertainness

Pileggi, Salvatore F.

doi:10.1016/j.procs.2015.05.378

Cited by 6 publications

(10 citation statements)

References 12 publications

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“…Thus, uncertainty operations must be applied to relationships between individuals but not those between classes. See [22] for a more detailed explanation.…”

Section: Translation Rulesmentioning

confidence: 99%

ByNowLife: A Novel Framework for OWL and Bayesian Network Integration

2019

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An ontology-based system can currently logically reason through the Web Ontology Language Description Logic (OWL DL). To perform probabilistic reasoning, the system must use a separate knowledge base, separate processing, or third-party applications. Previous studies mainly focus on how to represent probabilistic information in ontologies and perform reasoning through them. These approaches are not suitable for systems that already have running ontologies and Bayesian network (BN) knowledge bases because users must rewrite the probabilistic information contained in a BN into an ontology. We present a framework called ByNowLife, which is a novel approach for integrating BN with OWL by providing an interface for retrieving probabilistic information through SPARQL queries. ByNowLife catalyzes the integration process by transforming logical information contained in an ontology into a BN and probabilistic information contained in a BN into an ontology. This produces a system with a complete knowledge base. Using ByNowLife, a system that already has separate ontologies and BN knowledge bases can integrate them into a single knowledge base and perform both logical and probabilistic reasoning through it. The integration not only facilitates the unity of reasoning but also has several other advantages, such as ontology enrichment and BN structural adjustment through structural and parameter learning.

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“…Thus, uncertainty operations must be applied to relationships between individuals but not those between classes. See [22] for a more detailed explanation.…”

Section: Translation Rulesmentioning

confidence: 99%

ByNowLife: A Novel Framework for OWL and Bayesian Network Integration

2019

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“…In addition, currently there is not established foundation or standard for doing so. This may be due to a fundamental lack of consensus about an unified view of POs (Salvatore, 2015). For modeling uncertainty in ontologies, there are different approaches which are based on mathematical techniques of uncertainty: the Probabilistic theory, the fuzzy logic approach and the Dempster-Shafer theory.…”

Section: Related Workmentioning

confidence: 99%

“…It can be related to other data types external to the ontology through DataProperties. In practical cases, the schema of ontology is assumed to be deterministic (precise); however, the ontology population is not (Salvatore, 2015). This means that the classes, their hierarchies and the individuals of ontology are deterministic.…”

Section: Probabilistic Extensions Of Semantic Web Languagesmentioning

confidence: 99%

“…In the same way, the relation between two individuals or between an individual and data types is valid with a probability. Based on this assumption, the authors of (Salvatore, 2015) have tried to present how the OWL model can represent the uncertainty in ontologies by using only minor statistical extensions that are not altering the OWL philosophy. According to our knowledge, this work is the only one that uses OWL with minor statistical extensions for representing uncertain and probabilistic knowledge.…”

Section: Probabilistic Extensions Of Semantic Web Languagesmentioning

confidence: 99%

“…In practical cases, the schema of ontology is assumed to be deterministic (precise) however the ontology population (instantiation of ontology) is not (Salvatore, 2015). This means that the classes, their hierarchies and the individuals of ontology are deterministic.…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Building Probabilistic Ontology (Prob-Ont)

Hlel

Jamoussi

Hamadou

2017

International Journal of Information Technology and Web Engineering

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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).

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