Name
Abstract | ||
|---|---|---|
Social Tagging is a free but also uncontrolled way to index and organize Web 2.0 resources. Many works have been proposed to leverage such tagging information. However, although previous studies have shown that users could reach consensus on which tags should be attached to a resource, the study about the consensus showing how to use a tag is still lack. This paper proposes a text chance discovery and subjective Bayes based approach to model and detect consensus of tags. In the proposed approach, tag consensus is modeled using characteristics of resources annotated by the tag. Experiment results show that the proposed method could more properly capture to what extent the users have reached consensus about the tag in advance of usage frequency. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/DASC.2011.153 | DASC |
Keywords | Field | DocType |
previous study,web 2.0,tagging information,social tagging systems,experiment result,text chance discovery,usage frequency,consensus tags identification,web 2.0 resources,internet,social tagging,consensus tags,keygraph,subjective bayes,identifying consensus tags,social networking (online),tag consensus,chance discovery,probabilistic logic,semantics,taxonomy,ontologies,merging,web 2 0 | Ontology (information science),World Wide Web,Information retrieval,Computer science,Web 2.0,Probabilistic logic,Merge (version control),Semantics,The Internet,Bayes' theorem | Conference |
ISBN | Citations | PageRank |
978-1-4673-0006-3 | 0 | 0.34 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kening Gao | 1 | 13 | 8.79 |
| Yin Zhang | 2 | 3 | 2.75 |
| Bin Zhang | 3 | 213 | 41.40 |
| Xin Jin | 4 | 70 | 19.16 |
| Pengwei Guo | 5 | 0 | 0.34 |