RULK_NE

Abstract

A reliable representation of the user's knowledge state during a learning search session is crucial to understand their real information needs. When a search system is aware of such a state, it can adapt the search results and provide greater support for the user's learning objectives. A common practice to track the user's knowledge state is to consider the content of the documents they read during their search session(s). However, most current work ignores entity mentions in the documents, which, when linked to knowledge graphs, can be a source of valuable information regarding the user's knowledge. To fill this gap, we extend RULK - Representing User Knowledge in Search-as-Learning - with entity linking capabilities. The extended framework RULK represents and tracks user knowledge as a collection of such entities. It eventually estimates the user knowledge gain - learning outcome - by measuring the similarity between the represented knowledge and the learning objective. We show that our methods allow for up to 10% improvements when estimating user knowledge gains.