Document And Query Expansion Method With Dirichlet Smoothing Model For Retrieval Of Metadata Content In Digital Resource Objects

• Main Author: Alma’aitah, Wafa’ Za’al Mohammad
• Format: Thesis
• Language: English
• Published: 2020
• Subjects: QA75.5-76.95 Electronic computers. Computer science
• Online Access: http://eprints.usm.my/56033/1/Pages%20from%20WAFA%E2%80%99%20ZA%E2%80%99AL%20MOHAMMAD%20ALMA%E2%80%99AITAH%20cut.pdf; http://eprints.usm.my/56033/

In this thesis, an IR framework is proposed which consists of three main stages: enhanced document expansion (EDE) method, adaptive structured Dirichlet smoothing (ASDS) model, and semantic query expansion (SQE) method. The first stage involves proposing an EDE method in which a new procedure is introduced to increase each metadata unit content according to some specific steps by adding new information which is more relevant and closer to each metadata unit in each document while the second stage involves proposing an ASDS model that has two scenarios to improve the Dirichlet smoothing model. The first scenario is to enhance the model by taking into account of the document structure as in the proposed structured Dirichlet smoothing (SDS) model while the second scenario is to modify the parameters used in the model as in the proposed Adaptive Dirichlet smoothing (ADS) model. The third stage of the proposed framework involves the proposed SQE method to enhance the retrieval performance of DROs by improving the quality of candidate terms that are added semantically to the entire query term.