| Qiang Qiu|
(Alternative names for this author)
|Co-authors||Junping Zhu, Qu W., Tahir H., YanChun Zhang, Yang X., Yu S.|
|Authorship||Publications (2), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Qiang Qiu is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Designing a trust evaluation model for open-knowledge communities||British Journal of Educational Technology||English||2014||The openness of open-knowledge communities (OKCs) leads to concerns about the knowledge quality and reliability of such communities. This confidence crisis has become a major factor limiting the healthy development of OKCs. Earlier studies on trust evaluation for Wikipedia considered disadvantages such as inadequate influencing factors and separated the treatment of trustworthiness for users and resources. A new trust evaluation model for OKCs - the two-way interactive feedback model - is developed in this study. The model has two core components: resource trustworthiness (RT) and user trustworthiness (UT). The model is based on more interaction data, considers the interrelation between RT and UT, and better represents the features of interpersonal trust in reality. Experimental simulation and trial operation for the Learning Cell System, a novel open-knowledge community developed for ubiquitous learning, show that the model accurately evaluates RT and UT in this example OKC environment.||0||0|
|Building a text classifier by a keyword and Wikipedia knowledge||Keyword
|Lecture Notes in Computer Science||English||2009||Traditional approach for building text classifiers usually require a lot of labeled documents, which are expensive to obtain. In this paper, we propose a new text classification approach based on a keyword and Wikipedia knowledge, so as to avoid labeling documents manually. Firstly, we retrieve a set of related documents about the keyword from Wikipedia. And then, with the help of related Wikipedia pages, more positive documents are extracted from the unlabeled documents. Finally, we train a text classifier with these positive documents and unlabeled documents. The experiment result on 20Newsgroup dataset show that the proposed approach performs very competitively compared with NB-SVM, a PU learner, and NB, a supervised learner.||0||0|