Integration patterns in the use of metadata for data sense‐making during relevance evaluation: An interpretable deep learning‐based prediction.
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| Title: | Integration patterns in the use of metadata for data sense‐making during relevance evaluation: An interpretable deep learning‐based prediction. |
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| Authors: | Li, Qiao1, Wang, Ping2,3, Liu, Chunfeng2, Li, Xueyi2, Hou, Jingrui4 j.hou@lboro.ac.uk |
| Source: | Journal of the Association for Information Science & Technology. Mar2025, Vol. 76 Issue 3, p621-641. 21p. |
| Subjects: | Prediction models, Research funding, Task performance, Prompts (Psychology), Doctoral programs, Metadata, Deep learning, Conceptual structures, Research methodology, Information retrieval, Mathematical models, Theory |
| Abstract: | Integrating diverse cues from metadata to make sense of retrieved data during relevance evaluation is a crucial yet challenging task for data searchers. However, this integrative task remains underexplored, impeding the development of effective strategies to address metadata's shortcomings in supporting this task. To address this issue, this study proposes the "Integrative Use of Metadata for Data Sense‐Making" (IUM‐DSM) model. This model provides an initial framework for understanding the integrative tasks performed by data searchers, focusing on their integration patterns and associated challenges. Experimental data were analyzed using an interpretable deep learning‐based prediction approach to validate this model. The findings offer preliminary support for the model, revealing that data searchers engage in integrative tasks to utilize metadata effectively for data sense‐making during relevance evaluation. They construct coherent mental representations of retrieved data by integrating systematic and heuristic cues from metadata through two distinct patterns: within‐category integration and across‐category integration. This study identifies key challenges: within‐category integration entails comparing, classifying, and connecting systematic or heuristic cues, while across‐category integration necessitates considerable effort to integrate cues from both categories. To support these integrative tasks, this study proposes strategies for mitigating these challenges by optimizing metadata layouts and developing intelligent data retrieval systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the Association for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | Integrating diverse cues from metadata to make sense of retrieved data during relevance evaluation is a crucial yet challenging task for data searchers. However, this integrative task remains underexplored, impeding the development of effective strategies to address metadata's shortcomings in supporting this task. To address this issue, this study proposes the "Integrative Use of Metadata for Data Sense‐Making" (IUM‐DSM) model. This model provides an initial framework for understanding the integrative tasks performed by data searchers, focusing on their integration patterns and associated challenges. Experimental data were analyzed using an interpretable deep learning‐based prediction approach to validate this model. The findings offer preliminary support for the model, revealing that data searchers engage in integrative tasks to utilize metadata effectively for data sense‐making during relevance evaluation. They construct coherent mental representations of retrieved data by integrating systematic and heuristic cues from metadata through two distinct patterns: within‐category integration and across‐category integration. This study identifies key challenges: within‐category integration entails comparing, classifying, and connecting systematic or heuristic cues, while across‐category integration necessitates considerable effort to integrate cues from both categories. To support these integrative tasks, this study proposes strategies for mitigating these challenges by optimizing metadata layouts and developing intelligent data retrieval systems. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 23301635 |
| DOI: | 10.1002/asi.24961 |
