Hayden Wimmer

Applying Semantic Web Technologies to Ontology Alignment

Ontologies are the backbone of intelligent computing on the World Wide Web but also crucial in many decision support situations. Many sophisticated tools have been developed to support working with ontologies, including prominently exploiting the vast array of existing ontologies. A system called ALIGN is developed that demonstrates how to use freely available tools to facilitate ontology alignment. First two ontologies are built with the ontology editor Protege and represented in OWL. ALIGN then accesses these ontologies via Javas JENA framework and SPARQL queries. The efficacy of the ALIGN prototype is demonstrated on a drug-drug interaction problem. The prototype could readily be applied to other domains or be incorporated into decision support tools.

A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learning and data mining algorithms, namely neural networks, logistic regression, decision trees, and Bayesian classifiers. This applied research reveals practical implications for applying PPDM to data mining and machine learning and serves as a critical first step learning how to apply PPDM to machine learning algorithms and the effects of PPDM on machine learning. Results indicate that certain machine learning algorithms are more suited for use with PPDM techniques.

A Conceptual Model for Knowledge Marts for Decision Making Support Systems

This paper provides an integrated and comprehensive conceptual framework for knowledge based decision making support systems. Previous research has focused primarily on general decision support systems. The paper extends the previous work by presenting a framework to support specific decisions using knowledge marts that contain decision pertinent knowledge. A proposed methodology to test the effectiveness of this new model is proposed. The model presented provides much more specific knowledge support than previous systems.

Knowledge constrained evolutionary algorithms: a case study for financial investing

The purpose of this paper is to examine the role of domain knowledge in guiding evolution. The hypothesis examined in this paper is that the use of knowledge represented as a semantic network will bias mutation so that changes in structure measured by the semantic net correspond to changes in function. This hypothesis is tested utilising information and data from the finance domain. In this paper relevant literature is reviewed and an experimental framework is proposed which incorporates knowledge in evolution. An empirical investigation is presented to demonstrate the role of knowledge and gradualness in evolution. Future work will involve investigating methods to identify or construct a semantic network which is meaningful to humans as well as machines.

Integrating Knowledge Sources: An Ontological Approach

There has been a plethora of research in the area of knowledge portals, knowledge warehouses, ontologies, ontology creation and mapping, as well as the automatic creation and mapping of ontologies. While research exists in each respective area there is a lack of conceptual models that will integrate ontologies as a tool for disparate data source integration into knowledge portals or enterprise knowledge warehouses. The purpose of this work is to discuss different tools that have been developed in academic research and provide a conceptual model of how to implement these tools in relation to knowledge portals and warehouses.