Think outside the search box: A comparative study of visual and form-based query builders
May 09, 2022 Β· Declared Dead Β· π Journal of information science
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tanja Svarre, Tony Russell-Rose
arXiv ID
2205.04212
Category
cs.IR: Information Retrieval
Cross-listed
cs.HC
Citations
4
Venue
Journal of information science
Last Checked
4 months ago
Abstract
Knowledge workers such as healthcare information professionals, legal researchers, and librarians need to create and execute search strategies that are comprehensive, transparent, and reproducible. The traditional solution is to use proprietary query building tools provided by literature database vendors. In the majority of cases, these query builders are designed using a form-based paradigm that requires the user to enter keywords and ontology terms on a line-by-line basis and then combine them using Boolean operators. However, recent years have witnessed significant changes in human-computer interaction technologies and users can now engage with online information systems using a variety of novel data visualisation techniques. In this paper, we evaluate a new approach to query building in which users express concepts as objects on a visual canvas and compare this with traditional form-based query building in a lab-based user study. The results demonstrate the potential of visual interfaces to mitigate some of the shortcomings associated with form-based interfaces and encourage more exploratory search behaviour. They also demonstrate the value of having a temporary 'scratch' space in query formulation. In addition, the findings highlight an ongoing need for transparency and reproducibility in professional search and raise further questions around how these properties may best be supported.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Retrieval
R.I.P.
π»
Ghosted
π
π
Old Age
Neural Graph Collaborative Filtering
R.I.P.
π»
Ghosted
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
R.I.P.
π»
Ghosted
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
R.I.P.
π
404 Not Found
Graph Neural Networks for Social Recommendation
R.I.P.
π»
Ghosted
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted