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During the last several years, the volume of user- generated content on the web has skyrocketed. Today, the major Internet players such as MySpace, Wordpress, and YouTube all provide a variety of Web 2.0 based applications that allow users to post photos, share video, and manage blogs with multimedia content. Keyword
needs. In this paper, we present the design, architecture and implementation of an open-source keyword-based paradigm for the search of software resources in Grid infrastructures, called Minersoft. A key goal of Minersoft is to annotate automatically all the software resources with keyword-rich metadata. Using advanced
The Holy Quran, due to its unique style and allegorical nature, needs special attention about searching and information retrieval issues. The legacy keyword searching techniques are incapable of retrieving semantically relevant verses. In this paper, we address the deficiencies of key word based searching and the
precision ratio and novelty ratio than that of web search engines. Based on case studies, we found that there are four main types of query suggestion within digital library environments, namely spelling suggestion, hot keyword suggestion, personalized suggestion and semantic suggestion. These approaches are, however, hardly to
combine folksonomy, keyword and facet-based retrieval methods to retrieve software requirements related to users' interests. We add semantic ontology and users' feedback to obtain better software requirements that satisfy users' preferences to enhance software requirements retrieval performance. Finally, we demonstrate the
Keyword search for smallest lowest common ancestors (SLCAs) is a convenient method to retrieve information from XML documents for most of users, especially who have no knowledge or experience on XML. There have been many proposed algorithms solving SLCA problem through transforming XML documents into XML trees labeled
Mind maps are used by millions of people. In this paper we present how information retrieval on mind maps could be used to enhance expert search, document summarization, keyword based search engines, document recommender systems and determining word relatedness. For instance, words in a mind map could be used for
Traditional search engines simply match according to keywords and recommend information for all the users without considering user preferences. Thus, personalized retrieval technology becomes the ??hotspot?? of current research on information retrieval. On the groundwork of traditional search techniques, this paper
As computer aided learning (CAL) has become more and more popular in recent years, it is essential to provide a knowledge retrieval tool in any CAL environment. However, most CAL softwares available at present can only support searching with keywords. In this paper, we propose a technology that supports knowledge
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