Keyword Competition Approach In Ranked Document Retrieval - Sihombing, Poltak (2010)

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due to the availability of huge storage spaces, multiple storage devices and different storage media. The rapid growth of data in the database will eventually render the data unmanageable and cause problems in retrieval, where the users are unable to retrieve the right document. This is one of the most important problems in IRS. The use of keywords is one of the methods in IRS which can solve this problem. In this thesis, we propose a methodology in GA (Genetic Algorithm) which is known as Keyword Competition (KC) approach. KC is a competition scheme in finding the best keyword, known as ‘keyword solution’ (KS), among the available keywords. The keyword solution is then matched to the document collection in the database in order to retrieve the most relevant document. In this research, the collection of proceedings of BADAN TENAGA ATOM NASIONAL (BATAN) Indonesia, presented by University of Indonesia (UI), Jakarta was used as a standard dataset. We also propose a keyword based ranking scheme aimed to better rank the retrieved document in the spirit of presenting the most relevant document to the users. Keyword based ranking scheme consists of two (2) main phases; namely keyword solution matching and similarity percentage formulation. In the keyword matching process, the system will match those KS by finding the same words in the title, abstract & keyword of each document collection in the database. The similarity percentage formulation is used to rank the retrieved document based on the similarity value. The scheme was tested with two different fitness formulations, i.e. Jaccard’s function and Cosine’s function. We then compare the result of KC to the similarity level in Hopfield method. A prototype called Journal Browser System (JBS) based on this scheme was developed. The results collected from JBS provide the evidence that KC approach and keyword based ranking scheme give better performance compared to Hopfield method.