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Results diversification for keyword search on XML documents has attracted considerable attentions from research community in recent years. Though search results were diversified from different perspectives in the existing methods, the effects were still far away from satisfactory. This paper proposes a new way to
huge irrelevant search hits. In this paper, we propose an improved method for ranking of search results to reduce human efforts on locating interesting hits. The search results are re-ranked using adaptive user interest hierarchies (AUIH), which considers both investigator-defined keywords and user interest learnt from
addition, we use the keyword extracting method, which is based on the maximum entropy model, to get rid of the useless information. The experimental results show that the keyword extracting algorithm can get 70% precision, and the condition probabilistic based algorithm is more precise than the token-based algorithm. HIMA
Since short text is short of keywords and has sparse features, it brings about the similarity drift problem. The traditional clustering algorithms are usually ineffective and a waste of resources on dealing with short text stream. To overcome the above problems, this paper proposes an incremental clustering algorithm
process in which groups of semantically similar queries are identified. An efficient clustering algorithm called suffix tree clustering is developed in the study. Meanwhile, the keyword- based similarity measure is used for determining the closest cluster to the given query, and the Chinese synonymy is also considered in the
In this research, we used a proxy server to search for information related to the userpsilas browsed Web pages. From the records of the proxy server we constructed a profile of the userpsilas browsing habits. At the end of the userpsilas search subsystem, we will use content based concept to extract keywords to obtain
The user enters any query to find desired information. To discover number of user search goals and representing each goal with some keyword, we first infer user search goals for a query by clustering feedback sessions. For that, we use a concept of pseudo document, which is the revised version of feedback session
user to get specific information related to the submitted keyword. For this reason a new criterion is used in which feedback sessions are first generated from user clicked through logs. Using Feedback session a pseudo documents are generated by calculating TF-IDF (Term Frequency Inverse Data Frequency) vectors for each
Computer forensics is simply applies the computer investigation and analysis technique to the evidence of potential and the legal effect to determination and gain. It mainly includes the process of data access, data analysis, data submitted and so on. And the data analysis is the key link of computer forensics. It is faced with a question that we must extract useful information from the magnanimous...
analyzer to pick up information of service and use keywords to find out related services; then we cluster Web services according to the similarity of services; last, we select the appropriate Web service from list of services.
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