Master of Computing and Information Systems, Youngstown State University, 2008, Department of Computer Science and Information Systems

In this paper, we explore the concept of epistemic visualization in interrogative domains. Epistemic visualization is the process and result of developing visual models that capture the structure, content, justification and acquisition of knowledge obtained by a software agent in a knowledge-based system. The knowledge is the foundation in which the agent can respond to queries against a corpus containing questions and answers. The visualizations are therefore used to examine the quality of the software agent's knowledge. The visual models will include justification and commitment artifacts as well as knowledge acquisition flow. The visualization will demarcate the a priori and posteriori knowledge. The knowledge of the software agent is stored in epistemic structures which are knowledge representation schemes that supports the basic concepts of knowledge as defined by the tripartite analysis of knowledge. Epistemic visualization is used to analyze the quality of the knowledge of a software agent in an interrogative domain. For our purpose, interrogative domains are hearings, trials, interrogations, personality test or any document source in which the primary content is questions and answers pairs. In this paper, we introduce the Epistemic Structure Es that captures the agent's knowledge and the visualization of that epistemic structure using common visualization techniques.

Committee: Alina Lazar PhD (Committee Chair); John Sullins PhD (Committee Member); Yong Zhang PhD (Committee Member) Subjects: Artificial Intelligence; Computer Science; Information Systems; Linguistics; Technology

Master of Computing and Information Systems, Youngstown State University, 2008, Department of Computer Science and Information Systems

At Ctest Laboratories we are exploring the notion of automated conversion of the semi-structured text to an epistemic structure suitable for deductive inference. In this paper we will develop an epistemic structured representation for electronic transcripts ofinterrogative domains. We propose that knowledge which is typically not visible to keyword search or string matching, can be readily extracted from the an electronic transcript when it is given an appropriate epistemic structure. We introduce an Epistemic Structure Es and a process for converting a semi-structured transcript from and interrogative domain to Es. In this paper we restrict our discussion and analysis to transcripts that have been stored as semi-structured text. In particular we are interested in any knowledge that can be deduced by an interrogative agent from the content of an electronic transcript. Further we develop the notion of an interrogative agent that relies on epistemic justification as a condition for knowledge.

Committee: Alina Lazar PhD (Committee Chair); John Sullins PhD (Committee Member); Yong Zhang PhD (Committee Member) Subjects: Artificial Intelligence; Computer Science; Information Systems; Linguistics; Technology