research-article

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CoNotate: Suggesting Queries Based on Notes Promotes Knowledge Discovery

Authors:

Srishti Palani,

Stephen MacNeil,

Steven P. DowAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 726, Pages 1 - 14

https://doi.org/10.1145/3411764.3445618

Published: 07 May 2021 Publication History

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Abstract

When exploring a new domain through web search, people often struggle to articulate queries because they lack domain-specific language and well-defined informational goals. Perhaps search tools rely too much on the query to understand what a searcher wants. Towards expanding this contextual understanding of a user during exploratory search, we introduce a novel system, CoNotate, which offers query suggestions based on analyzing the searcher’s notes and previous searches for patterns and gaps in information. To evaluate this approach, we conducted a within-subjects study where participants (n=38) conducted exploratory searches using a baseline system (standard web search) and the CoNotate system. The CoNotate approach helped searchers issue significantly more queries, and discover more terminology than standard web search. This work demonstrates how search can leverage user-generated content to help people get started when exploring complex, multi-faceted information spaces.

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Cited By

Zhu RCheng CSong YCai Z(2024)Modeling Attentive Interaction Behavior for Web Content Identification in Exploratory Information SeekingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997508:4(1-28)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699750
Yen RZhao J(2024)Memolet: Reifying the Reuse of User-AI Conversational MemoriesProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676388(1-22)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676388
Zhu SHaisfield RLangen BChan J(2024)Patterns of Hypertext-Augmented SensemakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676338(1-17)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676338
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Index Terms

CoNotate: Suggesting Queries Based on Notes Promotes Knowledge Discovery
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

General Chairs:
Yoshifumi Kitamura
Tohoku University, Japan
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Aaron Quigley
University of New South Wales, Australia
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Katherine Isbister
University of California Santa Cruz, USA
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Takeo Igarashi
The University of Tokyo, Japan
,
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Pernille Bjørn
University of Copenhagen, Denmark
,
Steven Drucker
Microsoft Research, USA

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Cited By

Zhu RCheng CSong YCai Z(2024)Modeling Attentive Interaction Behavior for Web Content Identification in Exploratory Information SeekingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997508:4(1-28)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699750
Yen RZhao J(2024)Memolet: Reifying the Reuse of User-AI Conversational MemoriesProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676388(1-22)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676388
Zhu SHaisfield RLangen BChan J(2024)Patterns of Hypertext-Augmented SensemakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676338(1-17)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676338
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