RULKKG: Estimating User’s Knowledge Gain in Search-as-Learning Using Knowledge Graphs
Résumé
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.
We show that the proposed representation captures a complementary aspect of knowledge, thus helping to improve the user knowledge gain estimation when used in combination with other representations.
Fichier principal
Association_for_Computing_Machinery__ACM____SIG_Proceedings_Template__2_.pdf (517.71 Ko)
Télécharger le fichier
Origine | Fichiers produits par l'(les) auteur(s) |
---|