Relevance Models Based on the Knowledge Gap

TL;DR

This paper explores how modeling and incorporating the knowledge gap into search algorithms can enhance knowledge acquisition, proposing new methods and metrics to evaluate performance in this context.

Contribution

It introduces the concept of the knowledge gap into search system modeling and proposes approaches to improve search performance for knowledge acquisition tasks.

Findings

Modeling the knowledge gap can improve search relevance for learning.

Proposed metrics evaluate search effectiveness in knowledge acquisition.

Incorporating the knowledge gap enhances user learning outcomes.

Abstract

Search systems are increasingly used for gaining knowledge through accessing relevant resources from a vast volume of content. However, search systems provide only limited support to users in knowledge acquisition contexts. Specifically, they do not fully consider the knowledge gap which we define as the gap existing between what the user knows and what the user intends to learn. The effects of considering the knowledge gap for knowledge acquisition tasks remain largely unexplored in search systems. We propose to model and incorporate the knowledge gap into search algorithms. We plan to explore to what extent the incorporation of the knowledge gap leads to an improvement in the performance of search systems in knowledge acquisition tasks. Furthermore, we aim to investigate and design a metric for the evaluation of the search systems' performance in the context of knowledge acquisition tasks.