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With the advent of Web 2.0, RESTful web services are becoming increasingly popular to emphasize the web as platform. There are already many RESTful web services and the number of services is increasing rapidly. Thus, it can be difficult to find specific services using keyword based retrieval. To solve this problem, a
Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We
Most of the current information retrieval systems are mainly based on full text matching of keywords or topic-based classification, often return a large number of irrelevant information, and are unable to meet the user's request. Ontology-based semantic retrieval is a hot issue in current research. In this paper, the
With the rapid growth of web services, web services discovery becomes exceedingly important and challenging. Currently, many discovery approaches have been proposed such as keyword-based or VSM-based syntactic matching and ontology-based semantic matching. Syntactic matching approaches are clearly insufficient due to
Recently, Folksonomy attracts attentions as a new method to index large-scale image databases. In the Folksonomy-style image databases, they allows users to attach keywords to images as “tags”. Since tag words are uncontrolled, they have various and many kinds of tags associated with images. This is much
In the era of information explosion, information retrieval has become a bottleneck in information sharing and integration. However currently, the existing information retrieval methods are mainly based on keywords matching, which can not fully take advantage of the information context and potential knowledge. Although
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