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

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dc.contributor.authorAlma’aitah, Wafa’ Za’al Mohammad
dc.date.accessioned2022-08-12T01:49:12Z
dc.date.available2022-08-12T01:49:12Z
dc.date.issued2020-03
dc.description.abstractDigital resource objects (DRO) refer to information that are structured which elaborate, describe, and ease retrieval, usage and management of information resources. Lately, the need for accessing the content of DROs has been addressed differently by data retrieval (DR) and information retrieval (IR) research communities. DR is found to be inadequate in providing enriched metadata content and may fail to enhance the retrieval performance. 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. Extensive experiments were conducted to evaluate the effectiveness of the proposed methods, model and IR framework using the publicly available CHiC2013 collection. The experimental results show that the performances of the proposed EDE method, ASDS model, SQE method and IR framework improve by 10.5%, 11.3%, 8.1%, and 25.7% (mean average precision measure) respectively over conventional methods, models and frameworks.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/15802
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectComputer scienceen_US
dc.titleDocument And Query Expansion Method With Dirichlet Smoothing Model For Retrieval Of Metadata Content In Digital Resource Objectsen_US
dc.typeThesisen_US
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