to the capability of reflecting social perception on semantic of resources, folksonomy has been proposed to improve the social learning for education and scholar researching. However, its actual impact is significantl...to the capability of reflecting social perception on semantic of resources, folksonomy has been proposed to improve the social learning for education and scholar researching. However, its actual impact is significantly influenced by the semantic ambiguity problem of tags. So, in this paper, we proposed a novel way of detecting homonyms, one of the main sources of tag＇s semantic ambiguity problem, in noisy folksonomies. The study is based on two hypotheses： 1） Users having different interests tend to have different understanding of the same tag. 2） Users having similar interest tend to have common understanding of the same tag. Therefore, we firstly discover user communities according to users＇ interests. Then, tag contexts are discovered in subsets of folksonomy on the basis of user communities. The experimental results show that our method is effective and outperform the method finding tag contexts using all tags in folksonomy with overlapping clustering algorithm especially when various users having different interests are contained by the folksonomyo展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61300087National Science and Technology Ministry(ID:2013BAH01B00)
文摘to the capability of reflecting social perception on semantic of resources, folksonomy has been proposed to improve the social learning for education and scholar researching. However, its actual impact is significantly influenced by the semantic ambiguity problem of tags. So, in this paper, we proposed a novel way of detecting homonyms, one of the main sources of tag＇s semantic ambiguity problem, in noisy folksonomies. The study is based on two hypotheses： 1） Users having different interests tend to have different understanding of the same tag. 2） Users having similar interest tend to have common understanding of the same tag. Therefore, we firstly discover user communities according to users＇ interests. Then, tag contexts are discovered in subsets of folksonomy on the basis of user communities. The experimental results show that our method is effective and outperform the method finding tag contexts using all tags in folksonomy with overlapping clustering algorithm especially when various users having different interests are contained by the folksonomyo
