Natural language understanding has been a persistent challenge to researchers in various computer science fields, in a number of applications ranging from user support systems to entertainment and online teaching. A long term goal of the Artificial Intelligence field is to implement mechanisms that enable computers to emulate human dialogue. The recently developed ALICEbots, virtual agents with underlying AIML scripts, by A.L.I.C.E. foundation, use AIML scripts---a subset of XML---as the underlying pattern database for question answering. Their goal is to enable pattern-based, stimulus-response knowledge content to be served, received and processed over the Web, or offline, in the manner similar to HTML and XML. In this thesis, we describe a system that converts the AIML scripts to Prolog clauses and reuses them as part of a knowledge processor. The inference mechanism developed in this thesis is able to successfully match the input pattern with our clauses database even if words are missing. We also emulate the pattern deduction algorithm of the original logic deduction mechanism. Our rules, compatible with Semantic Web standards, bring structure to the meaningful content of Web pages and support interactive content retrieval using natural language.
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