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Traditional information gathering systems are mostly keyword-based that are lack of semantic comprehension and analysis ability and can't guarantee the comprehensiveness and accuracy of information gathering. This paper proposes Chinese patent information gathering model based on domain ontology, which can visualize
designed and implemented to resolve the problem of crossing language queries and retrieving images processes. It can greatly reduce lot of time and effort for the search. The experiments on diverse queries on Yahoo images search have shown that the proposed scheme can improve the images results for non-English keyword
In this paper, reclassification for the current classification through K-means would be implemented based on the feedback of Web usage mining in order to improve the accuracy of news recommendation and convergence of classification. It could extract most relative keywords and eliminate the disturbance of multi-vocal
extends the VSM based on keyword, it consider that the keywords in the page have different weight in the different position;Integrating the principles of Page-Rank, link analysis also considers that anchor text and website of the web page relevant with the theme.
Electronic Commerce has offered a convenient way for people to go shopping on the Internet. However, it is difficult for Internet customers to select a valuable item from the great number of various products available on line. When we use a keyword and search in a EC website, the ranking algorithm of products is
some problems, they tend to retrieve the information from the Web search engines. Many business search engines are efficient at identifying the best web sites for any given keyword query. Unfortunately, the information on the web is not always correct. Moreover, different web sites often provide different information on a
events. And a huge resource of text-based emotion can be found from the World Wide Web nowadays. This paper reports a study to investigate the effectiveness of using SVM (Support Vector Machine) on linguistic features considering emotion keywords and negative words, and classify a collection of blog posts sentences tagged
the word attributes are trained by the labeled training weblogs, and some keywords of a testing weblog are extracted as one part of the tags based on the probability distributions. Then the other part of the tags are obtained from the first part ones with the help of Latent Semantic Indexing (LSI) model. Experiments on a
Traditional automatic classifiers often conduct misclassifications. Folksonomy, a new manual classification scheme based on tagging efforts of users with freely chosen keywords can effective resolve this problem. Even though the scalability of folksonomy is much higher than the other manual classification schemes, the
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.