ScholarWiki system for knowledge indexing and retrieval
Identifieur interne : 000755 ( Main/Exploration ); précédent : 000754; suivant : 000756ScholarWiki system for knowledge indexing and retrieval
Auteurs : Xiaozhong Liu [États-Unis] ; Jian Qin [États-Unis] ; Miao Chen [États-Unis]Source :
- Proceedings of the American Society for Information Science and Technology [ 0044-7870 ] ; 2011.
Abstract
The goal of this research is to develop a tri‐dimensional metadata model and implement this model through the ScholarWiki system to combine the machine‐induced, user‐enhanced metadata for more effective knowledge discovery and information retrieval. The tri‐dimensional model captures the Structural, Descriptive, and Referential (SDR) metadata and incorporates them into a social media platform—ScholarWiki system. By allowing low‐barrier participation, scholars (both as authors and users) can participate in the knowledge and metadata editing and enhancing process and benefit from more accurate and effective information retrieval. The ScholarWiki system utilizes machine‐learning techniques that can automatically produce self‐enhanced metadata through learning the structural metadata that scholars contribute. The cumulated machine learning will add intelligence to automatically enhance and update the publication metadata Wiki pages.
Url:
DOI: 10.1002/meet.2011.14504801230
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 000301
- to stream Istex, to step Curation: 000301
- to stream Istex, to step Checkpoint: 000175
- to stream Main, to step Merge: 000757
- to stream Main, to step Curation: 000755
Le document en format XML
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<front><div type="abstract" xml:lang="en">The goal of this research is to develop a tri‐dimensional metadata model and implement this model through the ScholarWiki system to combine the machine‐induced, user‐enhanced metadata for more effective knowledge discovery and information retrieval. The tri‐dimensional model captures the Structural, Descriptive, and Referential (SDR) metadata and incorporates them into a social media platform—ScholarWiki system. By allowing low‐barrier participation, scholars (both as authors and users) can participate in the knowledge and metadata editing and enhancing process and benefit from more accurate and effective information retrieval. The ScholarWiki system utilizes machine‐learning techniques that can automatically produce self‐enhanced metadata through learning the structural metadata that scholars contribute. The cumulated machine learning will add intelligence to automatically enhance and update the publication metadata Wiki pages.</div>
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