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The representation and organization of learning objects should provide the user with easy access to the information in which he or she is interested. Traditionally information retrieval systems support keyword-based searching where the search engine returns a set of documents (or links to the documents). In the
It has become common to search necessary services and contents using the Internet, but it is difficult to find exactly what one is looking for through keywords as each service is described in just too many ways. We developed "laddering" search service system that matches the needs of the users with the search targets
In this paper we discuss the fundamental problem of information retrieval on the Web. Information on the Web is not semantically categorized and stored. This research focuses on applying semantic capabilities using ontology on search engine. By using ontology, search engine can search keywords that are conceptually
keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.
Because of ignoring the semantic information inside the keywords, the traditional searching engine based on the key words has low recall and precision. Aiming at this, some semantic retrieval system design ideas and the retrieval process are proposed in this paper.The key technologies-related in semantic 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.