News: Mohammad Ababneh receives PhD

Dr. Mohammad Ababneh Dissertation


The dissertation presents a design and a prototype of an Interactive Voice Response system for generating dynamic dialogs using Semantic Web technologies that can replace a human controlling access to secure resources. Item Response Theory was used to generate adaptive dialogs and to overcome challenges like recording, privacy, randomization, relevance, long dialogs and quantitative estimation of trust. Ontological and contextual reasoning were used to generate hard questions from inferred facts and relevant to context.

Dissertation Director: Dr. Duminda Wijesekera
Committee: Dr. Paulo Costa, Dr. Arun Sood, Dr. Rao Mulpori
Dissertation Acknowledgement was to Dr. Mark Pullen who encouraged me to apply for the PhD program at Mason, kept me interested after years of deferrals and supported me with a research assistantship when I finally got enrolled.

Mohammad Ababneh, Ph.D.
George Mason University, 2014
Dissertation Director: Dr. Duminda Wijesekera

A dialog system or a conversational agent provides a means for a human to interact with a computer system. Dialog systems use text, voice and other means to carry out conversations with humans in order to achieve some objective. Most dialog systems are created with specific objectives in mind and consist of preprogrammed conversations. The primary objective of this dissertation is to show that dialogs can be dynamically generated using semantic technologies. I show the feasibility by constructing a dialog system that can be specific to control physical entry points, and that can withstand an attempt to misuse the system by playing back previously recorded conversations. As a solution my system randomly generates questions with a pre-specified difficulty level and relevance, thereby not repeating conversations. In order to do so, my system uses policies to govern the entrance rules, and Item Response Theory to select questions derived from ontologies relevant to those policies. Furthermore, by using contextual reasoning to derive facts from a chosen context, my dialog system can be directed to generate questions that are randomized within a topic, yet relevant to the task at hand. My system design has been prototyped using a voice interface. The prototype demonstrates acceptable performance on benchmark data.