Dissertation Information for Seonghee KimNAME:
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SCHOOL: ADVISORS: COMMITTEE MEMBERS: MPACT Status: Fully Complete Title: Intelligent information retrieval using an inductive learning algorithm and a back-propagation neural network Abstract: This study demonstrated that the neural network inductive learning model (NNILM) can retrieve relevant documents from incomplete queries. In addition, this study showed that the neural network inductive learning model outperformed a vector space model even though incomplete queries were used with the NNILM. It presented a design for the application of inductive learning to information retrieval systems, in which an inductive algorithm was merged with a neural network to create a new information retrieval model. The performance results of this neural network inductive learning model were measured by comparing searches in response to complete queries with searches in response to incomplete queries in terms of three meausures: (1) the total number of relevant documents retrieved, (2) precision ratios, and (3) recall ratios. Furthermore, in order to demonstrate the predicted superiority of the neural network inductive learning model, its effectiveness in response to incomplete queries was compared to the effectiveness of the vector space model in response to complete queries. ADI (American Documentation Institute) documents and queries were selected to test the proposed model. The collections consist of 82 documents and 35 queries on the subject of Library and Information Science. |
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