RULKKG: Estimating User’s Knowledge Gain in Search-as-Learning Using Knowledge Graphs
Hadi Nasser, Dima El Zein, Célia da Costa Pereira, Cathy Escazut, Andrea G. B. Tettamanzi · 2024
In the context of search as learning, users engage in search sessions to fill their information gaps and achieve their learning goals. Tracking the user’s state of knowledge is therefore essential for estimating how close they are to achieve these learning goals. In this respect, we extend a recently proposed approach that uses the recognition of entities present in the text to track the user’s knowledge. Our approach introduces a more complete representation by considering both the entities and their relations. More precisely, we represent both the user’s knowledge and the user’s learning goals (or target knowledge) as knowledge graphs.