RULKNE: Representing User Knowledge State in Search-as-Learning with Named Entities

Autor: Dima El Zein, Arthur Câmara, Célia Da Costa Pereira, Andrea Tettamanzi
Přispěvatelé: Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Delft University of Technology (TU Delft), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Jacek Gwizdka, Soo Young Rieh
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, CHIIR 2023, Austin, TX, USA, March 19-23, 2023
CHIIR '23: ACM SIGIR Conference on Human Information Interaction and Retrieval
CHIIR '23: ACM SIGIR Conference on Human Information Interaction and Retrieval, Mar 2023, Austin, TX, United States. pp.388-393, ⟨10.1145/3576840.3578330⟩
DOI: 10.1145/3576840.3578330⟩
Popis: International audience; 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 NE 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.
Databáze: OpenAIRE
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