A Cognitive Agent Framework in Information Retrieval: Using User Beliefs to Customize Results

Abstract

This paper presents a framework for a cognitive agent in information retrieval that customizes the list of returned documents based on what the agent believes about the user knowledge. Throughout the interactions between the agent and the user, the agent builds its beliefs by extracting the content of the documents examined by the user. The agent's belief base consists of "simple beliefs" represented by the document's keywords as well as "contextual rules" that allow the agent to derive new beliefs about the user knowledge. The agent is therefore able to compare its own beliefs about the user knowledge with the knowledge conveyed by a given document, and thus understand if the document really contains useful information for the user or not. Finally, in case of inconsistency, the agent revises its belief base to restore consistency.

RULKKG: Estimating User’s Knowledge Gain in Search-as-Learning Using Knowledge Graphs

“…The recent theoretical framework proposed by El Zein et al in [18] uses a cognitive rule-based agent that adapts the document ranking based on both the user's current knowledge and search goal. Finally, the work on SAL presented by Gadiraju in [19], aims to improve the understanding of the knowledge acquired by users during the information search sessions.…”