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

Hadi Nasser (1) , Dima El Zein (1) , Célia da Costa Pereira (1) , Cathy Escazut (1) , Andrea G. B. Tettamanzi (2)

1 Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics

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.

Mots clés: User Knowledge; Knowledge Graphs; Interactive IR; Retrieval system ACM; CCS Concepts:; Information systems → Information retrieval; Information retrieval; • Human-centered computing → Human computer interaction (HCI) Search-As-Learning

Domaines: Recherche d'information [cs.IR]; Intelligence artificielle [cs.AI]

Hadi Nasser, Dima El Zein, Célia da Costa Pereira, Cathy Escazut, Andrea G. B. Tettamanzi. RULKKG: Estimating User’s Knowledge Gain in Search-as-Learning Using Knowledge Graphs. CHIIR 2024 : 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval, Mar 2024, Sheffield - UK, United Kingdom. pp.364-369, ⟨10.1145/3627508.3638331⟩. ⟨hal-04567545⟩