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.

https://www.mdpi.com/2078-2489/10/3/95/pdf

ByNowLife: A Novel Framework for OWL and Bayesian Network Integration

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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Decision Support Tool for the Agri-Food Sector Using Data Annotated by Ontology and Bayesian Network: A Proof of Concept Applied to Milk Microfiltration

Several areas have evolved with the evolution of technology. Telemedicine and home care are one of them. While there are many studies on home care, the overall problem of decision-making is not sufficiently addressed. In previous research, we proposed an ontological-based intervention system. Despite its abilities, reasoning on an ontology is currently limited to logic. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or assess the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In this article, we propose two approaches to combining probabilistic and logical reasoning within a home-based medical intervention system. The first, using probabilistic ontology PR-OWL. The second, providing a Jena API (Application Programming Interface). 

Combining Logical and Probabilistic Reasoning to Improve a home care platform

This chapter describes work on an integrated system that can assist analysts in exploring hypotheses using Bayesian analysis of evidence from a variety of sources. The hypothesis exploration is aided by an ontology that represents domain knowledge, events, and causality for Bayesian reasoning, as well as models of information sources for evidential reasoning. We are validating the approach via a tool, Magellan, that uses both Bayesian models and logical models for an analyst's prior knowledge about how evidence can be used to evaluate hypotheses. The ontology makes it possible and practical for complex situations of interest to be modeled and then analyzed formally.

Design Principles for Ontological Support of Bayesian Evidence Management.

Reasoning on an ontology is presently limited to the logical one. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or measure the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In these cases the users must integrate the probabilistic information of Bayesian networks (BN) in ontology manually. We propose a new approach to integrate the BN (more particularly Object-Oriented Bayesian Networks (OOBn)) with OWL by providing a Jena API (Application Programming Interface). OOBNs is an extension of the standard BN using the paradigm object. Our approach (i) uses the BN knowledge base for ontological enrichment, (ii) allows the adjustment of BN structures through structural and parametric learning based on ontological knowledge bases, (iii) integrate probabilistic information into SPARQL queries. In addition, we propose a solution in which we use a selection of instances according to the needs of the user described by a set of SPARQL requests.

A Jena API for combining ontologies and Bayesian object-oriented networks

Reasoning in ontology is currently limited to logical reasoning. It is because ontology does not have a standard for probabilistic reasoning. Various approaches have been made by researchers to add the ability for ontological reasoner to do probabilistic reasoning. The approach is done by combining ontology with Bayesian network that does have probabilistic reasoning abilities. This study mapped out various approaches performed in combining ontologies with Bayesian networks to realize logical and probabilistic reasoning simultaneously. We use a systematic literature review method to identify the primary studies on combining ontology with Bayesian network following a predefined review protocol. We searched from four indexing services (SCOPUS, IEEE Xplore, ACM Digital Library, and SpringerLink) and got the result of 74 papers accepted for the review. We extracted properties from these studies and found 8 motivations of the studies, 5 contexts, 4 factors involved, and 5 techniques used in combining ontology with Bayesian network. The aim and the context are clearly stated in most of the studies, while most of the authors did not completely discuss the threats in their papers. As for the method and findings, most of the studies describe and discuss it moderately.

T. Basaruddin

Papers

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