Name
Abstract | ||
|---|---|---|
AbstractScholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata structural keywords using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars both as authors and users can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1002/asi.23013 | Periodicals |
Keywords | Field | DocType |
Metadata Generation,Human Intelligence,Artificial Intelligence,Natural Language Processing NLP,Scholar Publication,User Evaluation,Cross-Folder Validation | Data mining,Metadata,Metadata repository,World Wide Web,Social media,Information retrieval,Geospatial metadata,Human intelligence,Computer science,Meta Data Services,Data element,Recursion | Journal |
Volume | Issue | ISSN |
65 | 6 | 2330-1635 |
Citations | PageRank | References |
0 | 0.34 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaozhong Liu | 1 | 368 | 48.27 |
| Chun Guo | 2 | 79 | 5.96 |
| Lin Zhang | 3 | 91 | 24.95 |