A Context-Based Technique Using Tag-Tree for an Effective Retrieval From a Digital Literature Collection

Abstract

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.

A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

“…It is difficult to search the entire corpus to extract relevant knowledge. Contextual query [15] analysis is also tedious as each user looks at the data from a different perspective. Extracting synopsis knowledge from PubMed is time consuming and work intensive.…”

Evaluating the effectiveness of thesauri in digital information retrieval systems

“…Moreover, many researchers report its use in all three purposes (Atherton, 2002; Binding and Tudhope, 2004; Blocks et al , 2006; Hienert et al , 2011; Petrič et al , 2011). Furthermore, a couple of researchers used it in both indexing and searching (Shiri et al , 2011; Soo et al , 2003; Torres and Reis, 2008) and a few reported its use only in searching (Bakar and Rahman, 2003; Feki et al , 2014; Sarmento et al , 2008; Thangaraj and Gayathri, 2013).…”

“…In recent years, there has been an increased emphasis on evaluating the effectiveness of thesauri in digital information systems. There are a growing number of publications which report the methodological approach and findings of such evaluation studies (Alonso Gaona García et al , 2014; Hienert et al , 2011; Lüke et al , 2012; Shiri et al , 2013; Thangaraj and Gayathri, 2013). This paper tries to identify as many such evaluation studies as possible to analyze their methodological approaches and findings towards gaining insights into the effectiveness of thesauri in digital information systems.…”

Text Preprocessing using Annotated Suffix Tree with Matching Keyphrase

“…Usually, keyphrases are features of text collection in which their numbers are calculated based on their presence for values in the features. Keyphrases have been used in many applications such as for determining index of a digital library [7]; for supportinga text-based decision system in financial sequence prediction [8] andquestion answering system [9]; for ranking topical keyphrases from content-representative document titles [10]; for clustering document [11] and text from structured data [12]; for applying in queryoriented summarization [13], information extraction [14] [15], text categorization [16] and information retrieval [17].…”