Chapter 2 LITERATURE REVIEW
2.4 Integrating Ontologies and Bayesian Networks
2.4.3 Current Approaches to Ontology and Bayesian Networks Integration
BNs are a potentially useful modelling paradigm to model factors that influence treatment adherence behaviour and their cause for the purpose of prediction. BNs are used to represent vague and probabilistic causal relationships between different variables [90] [24]. They can potentially be adopted for representing a belief network that is useful for predicting adherence risk and may be used as the basis for decision support tools to help TB programme coordinators identify and/or predict potential treatment default behaviour. Although, BN is a strong tool for modelling uncertainty, it lacks the capability to represent the semantics of variables and their states. Also, developing such networks for adherence behaviour requires significant modelling efforts, including identification of influencing factors, formalizing these factors to form the network’s structure, determination of the weighting for the conditional probabilities, and consolidating evidence for learning the network. Expert knowledge and primary data sources are important requirements for BNs’ construction and they are difficult to harmonise, particularly when dealing with unstructured data [73].
Ontologies, on the other hand, can be useful for consolidation and representation of categorical knowledge from an unstructured source of data as they have significant capability for structuring and classifying concepts and providing connections and relationships between concepts in an application domain [14]. Although an ontology is very useful for the conceptualization of an adherence knowledge base system, some ontology languages such as OWL 2 lack the capability to represent uncertainty, which is an integral part of adherence risk prediction.
There have been earlier efforts to integrate the dimension of uncertainty into semantics by trying to combine ontologies and BNs for various purposes. Larik and Haider [20] classified these efforts into four main categories based on the purpose of the ontologies and BN integration. The categories identified by Larik and Haider [20] are: ontology mapping enhancement; ontology reasoning enhancement with BNs; semi-automated construction of Bayesian networks; and ontology language enhancement [20]. Some of the existing approaches to ontologies-BN integration are discussed below.
2.4.3.1 Ontology Mapping Enhancement
The aim of an Ontology Mapping Enhancement (OMEN) [91] approach is to resolve the semantic heterogeneity of similar ontology concepts. OMEN [91] was designed specifically for mapping two similar ontologies using BNs. It uses a pre-specified threshold to match the initial probability of two ontologies being merged. The probabilistic constraints that are used for the enhancement will be defined in an OWL file. The constraints are used to generate nodes for all the matches found as well as the mapping between the pairs of matching concepts. A set of meta-rules are then defined for the construction of the CPT [91].
2.4.3.2 BayesOWL
BayesOWL is a probabilistic framework developed for modelling uncertainty in the Semantic Web [49] [20] [92]. In order to describe uncertainty in a consistent manner, Ding et al (2006) [92] proposed BayesOWL for extending OWL’s capability to handle probabilistic reasoning.
BayesOWL is a probabilistic extension to OWL and defines the probabilistic relatedness of distinct classes [92]. BayesOWL was developed to enhance probabilistic constraints and has been used to map concepts between similar ontologies.
2.4.3.3 SWAP Uncertainty Ontology
The SWAP-Uncertainty ontology is an extension of the BayesOWL ontology that was specifically developed for managing uncertainty associated with sensor observations in the Sensor Web [49].
The extension was made to address some of the shortfalls of BayesOWL in representing the uncertainty of the sensor web [49]. The extension includes an extension of BayesOWL classes for handling complexities of sensor observations. For instance, the influence relationship between variables was extended for building BN graphs automatically from the variables. The state class was also extended to allow for capturing of discrete range states and explicit declaration of all variable states. The condition class was extended to facilitate declaration of multiple states of influencing variables when declaring condition probability for a node in the network.
2.4.3.4 Ontology Reasoning Using Bayesian Networks
This is an approach introduced by Andrea and Franc, 2009 [93] for performing reasoning on an ontology using BNs [20]. The approach is not to extend an OWL file with BNs, it only uses information stored in the domain ontology for constructing the corresponding BN, which is in turn used as a probabilistic reasoner for the ontology [93] [20]. It comprises three basic steps:
structure construction; CPT construction; and probabilistic reasoning with the BN inference. The first step is to construct a structural, two level BN from the TBox of the ontology by creating a two level BN for the reasoning. The second step is to construct the CPT from the ABox of the ontology. The third and last step is to perform probabilistic ontological reasoning, using BN inference.
2.4.3.5 Semi-automated Construction of Semantic Bayesian Network
An approach for a semi-automated construction of a semantic BN was introduced by Fenz and Hudec [94] as a means of representing domain concept uncertainty in order to provide a structured representation of the knowledge required to construct a BN model. The approach was introduced because of the recognition of the knowledge requirement challenges in constructing a BN [95], which include the determination of the factors/ variables, the relationship between the variables, and the generation of the condition probability required for the network. The steps in the approach include mapping of domain concepts, implementing the concepts as an ontology, applying
experts’ intuitive methods for transforming the ontology into BNs primitives and construction of CPT by modellers [94], [95].
2.4.3.6 Probabilistic Extension to the Web Ontology Language (PR-OWL)
The Probabilistic extension to OWL (PR-OWL) [90] approach was developed to provide a principled means of modelling uncertainty that is lacking in the OWL technologies. PR-OWL was developed to aid the semantic web vision to actualise its aim of providing a sound and principled means of representing and reasoning with uncertainty. PR-OWL seeks to remedy the incapability of OWL in handling uncertainty by developing a BN framework for probabilistic ontologies and a reasoning service [23]. PR-OWL is a general framework that was based on Multi-Entity Bayesian Networks logic which integrates first order logic with BNs [90] [20]. It was designed as a full first-order probabilistic logic in an attempt to address the deterministic classical logic’s current limitations [23]. PR-OWL provides support for any application that can benefit from ontology-based probabilistic inference, using an ontology-based BN description UnBBayes8. The weakness of PR-OWL lies in the fact that modellers first have to understand the concept of MEBN theory [20].
2.4.3.7 Bayesian Description Logic
Bayesian Description Logics (BDLs) are extensions of classic Description Logics (DLs) with contextual probabilities encoded in a BN [76]. BDL is designed to handle uncertainty that is expressed through a BN. The reasoning tasks of a DL are extended to consider contextual and probabilistic information [76]. BDL is based on the light-weight description logic, EL, which was extended to express uncertainty. BDL was developed on the assumption that certain knowledge is dependent on an uncertain situation or context. That is, every axiom is associated with a context with the intended meaning of being true if the context holds [96]. BDL approach can be applied for automated mapping of information integration in order to avoid human intervention [97].
8 http://unbbayes.sourceforge.net