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Web service discovery is a vital problem in service computing with the increasing number of services. Existing service discovery approaches merely focus on WSDLbased keyword search, semantic matching based on domain knowledge or ontologies, or QoS-based recommendations. The keyword search omits the underlying
Latent Dirichlet Allocation, which is a non-supervised learning method, can be used for topic detection, automatic text categorization, keyword extraction and so on. It only focuses on the text itself, not considering other external correlation properties. External association property refers to some structured
providing support for calculating the similarity between query keywords and documents. Our experiments on PubMed medical article collections show that the semantic-based multi-analysis approach is feasible and efficient compared with other traditional approaches in medical retrieval.
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.