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There are hundreds of millions of images available on the current World Wide Web. The demand for image retrieval online is growing dramatically. For multimedia documents, the typical keyword-based retrieval method has encountered problems mainly in the areas of: 1) the quality of the search result; 2) the usage of the
The demand for image retrieval and browsing online is growing dramatically. There are hundreds of millions of images available on the current World Wide Web. For multimedia documents, the typical keyword-based retrieval methods assume that the user has an exact goal in mind in searching a set of images whereas users
and completeness through sense disambiguation and contextual meta-data prepossessing. Our schemes exploits a linguistic ontology identifying query relevant homographs used to construct sense specific keyword sets allowing for enhanced image search and result ranking via the calculation of relatedness between query
introduction of Ranking Evaluator. Compared to previous works, another important idea we proposed is that we use a Search Arbiter to judge whether the query is answered by Keyword-based Search Engine or Ontology Search Engine, which is based on whether there is not enough ontology knowledge or not. Also, key techniques are
In the World-Wide Web context, availability of software components increases the possibility of applying a reuse approach in software development. Thus, component retrieval is a key problem, both for software industry and for end-users. Several problems should be solved: components formalisms are very different from one source to another, knowledge for guiding components retrieval is often poor and...
with different objectives, contexts, granularities, and educational values. This presents a wide range of options to the user. Due mainly to the amount of options available and the lack of qualification standards, it is difficult use keywords to select learning objects that will maximize the learning process in a
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