A CONTEXT-BASED TECHNIQUE USING TAG-TREE FOR AN EFFECTIVE RETRIEVAL FROM A DIGITAL LITERATURE COLLECTION

The increasing growth of information in online digital libraries causes an increasing need to develop techniques to retrieve. In the digital library, findability-finding the user required information is a hectic task than those of usability. The major issues in findability are (a) topic diffusion: results of a traditional keyword based search, often leads to multiple topic areas, some of which are not interested to user; (b) lack of scoring mechanism: at present, digital libraries lack effective and accurate publication rankings. Thus the users are forced to scan a large result set, which leads them to miss the important ones; providing accurate publication scores can help users in reducing the time spent in searching and (c) selecting search keywords: users spend more time to choose their search keywords, which will express their information need. This study proposes TAG, a new context based retrieval technique that controls the topic diversity and overcomes the above mentioned issues effectively. Using IEEE publications as the test bed and IEEE thesaurus terms as context, our experiments indicate that the proposed retrieval technique effectively produces output results and considerably reduces the resultant set.