The hybridised indexing method for research-based information retrieval

Abstract

An information retrieval system (IRS) is used to retrieve documents based on an information need. The IRS makes relevance judgements by attempting to match a query to a document. As IRS capabilities are indexing design dependent, the hybrid indexing method (IRS-H) is introduced. The objectives of this article are to examine IRS-H (as an alternative indexing method that performs exact phrase matching) and IRS-I, regarding retrieval usefulness, identification of relevant documents, and the quality of rejecting irrelevant documents by conducting three experiments and by analysing the related data. Three experiments took place where a collection of 100 research documents and 75 queries were presented to: (1) five participants answering a questionnaire, (2) IRS-I to generate data and (3) IRS-H to generate data. The data generated during the experiments were statistically analysed using the performance measurements of Precision, Recall and Specificity, and one-tailed Student’s t-tests. The results reveal that IRS-H (1) increased the retrieval of relevant documents, (2) reduced incorrect identification of relevant documents and (3) increased the quality of rejecting irrelevant documents. The research found that the hybrid indexing method, using a small closed document collection of a hundred documents, produced the required outputs and that it may be used as an alternative IRS indexing method.

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

“…The system’s structure covers three core modules: image management, image retrieval, and image review [ 28 – 30 ]. Among them, the image management module focuses on the grouping and classification of existing image data, and performs data analysis and visual display simultaneously.…”

“…In addition, according to the specific functional requirements of the system, the functional structure of the cultural relics image retrieval system is constructed, as revealed in Fig 4 . The system's structure covers three core modules: image management, image retrieval, and image review [28][29][30]. Among them, the image management module focuses on the grouping and classification of existing image data, and performs data analysis and visual display simultaneously.…”