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

Dima El Zein, Arthur Câmara, Célia Da Costa Pereira, Andrea Tettamanzi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

11 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationCHIIR 2023 - Proceedings of the 2023 Conference on Human Information Interaction and Retrieval
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Pages388-393
Number of pages6
ISBN (Electronic)979-8-4007-0035-4
DOIs
Publication statusPublished - 2023
Event8th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2023 - Austin, United States
Duration: 19 Mar 202323 Mar 2023

Publication series

NameCHIIR 2023 - Proceedings of the 2023 Conference on Human Information Interaction and Retrieval

Conference

Conference8th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2023
Country/TerritoryUnited States
CityAustin
Period19/03/2323/03/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Interactive IR
  • Named Entities
  • Retrieval system
  • Search-As-Learning
  • User Knowledge

Fingerprint

Dive into the research topics of 'RULKNE: Representing User Knowledge State in Search-as-Learning with Named Entities'. Together they form a unique fingerprint.

Cite this