An ontology-driven approach for structuring scientific knowledge for predicting treatment adherence behaviour: a case study of tuberculosis in Sub-Saharan African communities.

Doctor of Philosophy in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban 2016.Poor adherence to prescribed treatment is a complex phenomenon and has been identified as a major contributor to patients developing drug resistance and failing treatment in sub-Saharan African countries. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that may vary drastically between communities in different regions. Computer based predictive models can be used to identify individuals and communities at risk of non-adherence and aid in supporting resource allocation and intervention planning in disease control programs. However, constructing effective predictive models is challenging, and requires detailed expert knowledge to identify factors and determine their influence on treatment adherence in specific communities. While many clinical studies and abstract conceptual models exist in the literature, there is no known concrete, unambiguous and comprehensive computer based conceptual model that categorises factors that influence treatment adherence behaviour. The aim of this research was to develop an ontology-driven approach for structuring knowledge of factors that influence treatment adherence behaviour and for constructing adherence risk prediction models for specific communities. Tuberculosis treatment adherence in sub-Saharan Africa was used as a case study to explore and validate the approach. The approach provides guidance for knowledge acquisition, for building a comprehensive conceptual model, its formalisation into an OWL ontology, and generation of probabilistic risk prediction models. The ontology was evaluated for its comprehensiveness and correctness, and its effectiveness for constructing Bayesian decision networks for predicting adherence risk. The approach introduces a novel knowledge acquisition step that guides the capturing of influencing factors from peer-reviewed clinical studies and the scientific literature. Furthermore, the ontology takes an evidence based approach by explicitly relating each factor to published clinical studies, an important consideration for health practitioners. The approach was shown to be effective in constructing a flexible and extendable ontology and automatically generating the structure of a Bayesian decision network, a crucial step towards automated, computer based prediction of adherence risk for individuals in specific communities

Run-time risk management in adaptive ICT systems

We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers

Representing Network Trust and Using It to Improve Anonymous Communication

Motivated by the effectiveness of correlation attacks against Tor, the censorship arms race, and observations of malicious relays in Tor, we propose that Tor users capture their trust in network elements using probability distributions over the sets of elements observed by network adversaries. We present a modular system that allows users to efficiently and conveniently create such distributions and use them to improve their security. The major components of this system are (i) an ontology of network-element types that represents the main threats to and vulnerabilities of anonymous communication over Tor, (ii) a formal language that allows users to naturally express trust beliefs about network elements, and (iii) a conversion procedure that takes the ontology, public information about the network, and user beliefs written in the trust language and produce a Bayesian Belief Network that represents the probability distribution in a way that is concise and easily sampleable. We also present preliminary experimental results that show the distribution produced by our system can improve security when employed by users; further improvement is seen when the system is employed by both users and services.Comment: 24 pages; talk to be presented at HotPETs 201

Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.Comment: Workshop on Theory of Belief Functions, Brest: France (2010

Dealing with uncertain entities in ontology alignment using rough sets

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision