Abstract
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.
Highlights
In Web Ontology Language (OWL), reasoning in ontologies is supported using a sub-language that accommodates a description logic (DL)-based reasoning called OWL DL [1]
Of the features of previous frameworks, there are three aspects that have never before been successfully implemented in past research: ontology enrichment based on Bayesian network (BN) knowledge bases, the adjustment of BN structures through structural and parameter learning based on ontology knowledge bases and support for an integrated probabilistic clause in SPARQL query format
This is because ByNowLife transforms BNs into ontologies before reasoningbefore is performed on isthe ontology on knowledge base.knowledge
Summary
In Web Ontology Language (OWL), reasoning in ontologies is supported using a sub-language that accommodates a description logic (DL)-based reasoning called OWL DL [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. For systems that already have ontologies and BN knowledge bases, it is unnecessary to rewrite the probabilistic information contained in the BN into the OWL; it will require significant effort, especially if the BN consists of thousands of nodes and relations. In cases such as this, a machine capable of transforming the information contained in the BN into OWL and vice versa is needed.
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