A Systematic Literature Review on Combining Ontology with Bayesian Network to Support Logical and Probabilistic Reasoning

Setiawan, Foni Agus; Budiardjo, Eko K.; Basaruddin, T.; Aminah, Siti

doi:10.1145/3178212.3178223

Cited by 5 publications

(4 citation statements)

References 65 publications

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“…Supported by a description logic language such as the Web Ontology Language (OWL), ontology-based information systems enable automatic logical reasoning. Multiple approaches have been developed to manage probabilistic reasoning in ontology, (Setiawan et al 2017). Several languages have emerged to tackle BNs in ontologies such as PR-OWL (Carvalho et al 2017), BayesOWL (Pan et al, 2005) or OntoBayes (Yang and Calmet, 2005) extending OWL.…”

Section: Related Studies and The Utility For Microfiltration Plantsmentioning

confidence: 99%

Decision Support Tool for the Agri-Food Sector Using Data Annotated by Ontology and Bayesian Network

Baudrit

Buche

Leconte

et al. 2022

International Journal of Agricultural and Environmental Information Systems

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The scientific literature is a valuable source of information for developing predictive models to design decision support systems. However, scientific data are heterogeneously structured expressed using different vocabularies. This study developed a generic workflow that combines ontology, databases and computer calculation tools based on the theory of belief functions and Bayesian networks. The ontology paradigm is used to help integrate data from heterogeneous sources. Bayesian network is estimated using the integrated data taking into account their reliability. The proposed method is unique in the sense that it proposes an annotation and reasoning tool dedicated to systematic analysis of the literature, which takes into account expert knowledge of the domain at several levels: ontology definition, reliability criteria and dependence relations between variables in the BN. The workflow is assessed successfully by applying it to a complex food engineering process: skimmed milk microfiltration. It represents an original contribution to the state of the art in this application domain.

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Section: Related Studies and The Utility For Microfiltration Plantsmentioning

confidence: 99%

Decision Support Tool for the Agri-Food Sector Using Data Annotated by Ontology and Bayesian Network

Baudrit

Buche

Leconte

et al. 2022

International Journal of Agricultural and Environmental Information Systems

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“…Systematic literature review (SLR) results [3] reveal four published frameworks related to the integration of ontologies with BNs: BayesOWL, Multi-Entity Bayesian Network/Probabilistic OWL (MEBN/PR-OWL), OntoBayes, and HyProb-Ontology. Each framework has pros and cons.…”

Section: Related Workmentioning

confidence: 99%

“…However, the type of required reasoning is not always purely logical (true or false, 1 or 0) but can also be probabilistic (the degree of certainty or probability that an event will happen, represented by a value between 0 and 1). Bayesian networks (BNs) have been chosen by many previous researchers as models for managing probabilistic reasoning in ontologies [3]. This is because in addition to its ability to perform probabilistic reasoning with prior knowledge, a BN has a graphical structure like that supported by ontological representation in OWL.…”

Section: Introductionmentioning

confidence: 99%

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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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Combining Logical and Probabilistic Reasoning to Improve a home care platform

Gueddes

Mahjoub

2022

2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)

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A Systematic Literature Review on Combining Ontology with Bayesian Network to Support Logical and Probabilistic Reasoning

Cited by 5 publications

References 65 publications

Decision Support Tool for the Agri-Food Sector Using Data Annotated by Ontology and Bayesian Network

Decision Support Tool for the Agri-Food Sector Using Data Annotated by Ontology and Bayesian Network

ByNowLife: A Novel Framework for OWL and Bayesian Network Integration

Combining Logical and Probabilistic Reasoning to Improve a home care platform

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