As high quality descriptors of web page semantics, social annotations or tags have been used for web document clustering and achieved promising results. However, most web pages have few tags （less than 10）. This spa...As high quality descriptors of web page semantics, social annotations or tags have been used for web document clustering and achieved promising results. However, most web pages have few tags （less than 10）. This sparsity seriously limits the usage of tags for clustering. In this work, we propose a user-related tag expansion method to overcome this problem, which incorporates additional useful tags into the original tag document by utilizing user tagging data as background knowledge. Unfortunately, simply adding tags may cause topic drift, i.e., the dominant topic（s） of the original document may be changed. To tackle this problem, we have designed a novel generative model called Folk-LDA, which jointly models original and expanded tags as independent observations. Experimental results show that 1） our user-related tag expansion method can be effectively applied to over 90% tagged web documents; 2） Folk-LDA can alleviate topic drift in expansion, especially for those topic-specific documents; 3） the proposed tag-based clustering methods significantly outperform the word-based methods., which indicates that tags could be a better resource for the clustering task.展开更多
Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web the...Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web theme information, this paper proposes and implements dynamic conceptual clustering algorithm and merging algorithm for Web documents, and also analyses the super performance of the clustering algorithm in efficiency and clustering accuracy. Key words conceptual clustering - clustering center - dynamic conceptual clustering - theme - web documents clustering CLC number TP 311 Foundation item: Supported by the National “863” Program of China (2002AA111010, 2003AA001032)Biography: WANG Yun-hua(1979-), male, Master candidate, research direction: knowledge engineering and data mining.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61070111
文摘As high quality descriptors of web page semantics, social annotations or tags have been used for web document clustering and achieved promising results. However, most web pages have few tags （less than 10）. This sparsity seriously limits the usage of tags for clustering. In this work, we propose a user-related tag expansion method to overcome this problem, which incorporates additional useful tags into the original tag document by utilizing user tagging data as background knowledge. Unfortunately, simply adding tags may cause topic drift, i.e., the dominant topic（s） of the original document may be changed. To tackle this problem, we have designed a novel generative model called Folk-LDA, which jointly models original and expanded tags as independent observations. Experimental results show that 1） our user-related tag expansion method can be effectively applied to over 90% tagged web documents; 2） Folk-LDA can alleviate topic drift in expansion, especially for those topic-specific documents; 3） the proposed tag-based clustering methods significantly outperform the word-based methods., which indicates that tags could be a better resource for the clustering task.
文摘Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web theme information, this paper proposes and implements dynamic conceptual clustering algorithm and merging algorithm for Web documents, and also analyses the super performance of the clustering algorithm in efficiency and clustering accuracy. Key words conceptual clustering - clustering center - dynamic conceptual clustering - theme - web documents clustering CLC number TP 311 Foundation item: Supported by the National “863” Program of China (2002AA111010, 2003AA001032)Biography: WANG Yun-hua(1979-), male, Master candidate, research direction: knowledge engineering and data mining.
