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One of the challenging problem that Web service technology is now facing is effective service discovery. To solve the deficiencies of Web service description, matching and choosing under WSDL language, this paper presents a web service discovery method based on keyword clustering and concept expansion, mainly from the
This paper proposes a structure that automatically analyzes the parameters of Chinese test items. This structure utilizes latent semantic analysis (LSA) to analyze the relationships of keywords among all test items in an item bank. It also uses the similarity measure to calculate the similarity degree of keywords. We
using keywords graph to contribute special techniques for exploring those groups and the relationships among them. Interactions between users and the created keywords graph are also provided. Compared to other applications on blog visualization, our approach utilized the ontology knowledge to analysis and automatically
This paper presents a novel semi-supervised learning method which can make use of intra-image semantic context and inter-image cluster consistency for image categorization with less labeled data. The image representation is first formed with the visual keywords generated by clustering all the blocks that we divide
To get semantic related searching results based on simple keywords, XML search engine not only need to search the matched nodes but also need to check whether those matched nodes are semantic related nodes in XML tree. Since the judgment on the semantic related nodes might cost much time, we first use mining
This paper introduces a new technique of document clustering based on frequent senses. The proposed system, GDClust (graph-based document clustering) works with frequent senses rather than frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and
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