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The field of computer science has gathered more weight since the advent of the internet, not only to the computer scientist and engineers but also to professionals of all categories. This is so because of the remarkable benefits it offers to the body of knowledge. Sharing information has been made easy, even among people from various parts of the world as the World Wide Web becomes a dumping zone for various categories of information. Searching for information on the internet to study state of the art for research purpose to a large extent depends on our ability to track all related topics and classify them into groups of similar topics. As information grows rapidly over the web, it becomes more difficult for researchers to find the information they are looking for. The rapid growth of information on the web and the inadequacy of the conventional search engines to retrieve relevant information based on user’s request have motivated this research. This work extends earlier Fuzzy Information Retrieval models by adding more fuzzy linguistic values, fuzzy variables, using different membership functions, using rules that consider the document structure and optimizing the throughput using Nuero-fuzzy system. The document is quantified by describing it using features/linguistic variables that contribute most to its relevance to the query like the Lexical density, term weight, and document similarity vector as well as word ratio. Linguistic values are assigned to each of these variables that associate them with membership degrees. An ANFIS inference engine was built using the seugeno method that handle these variables to measure the degree of document relevance to the query. The essence of applying the neuro-fuzzy techniques is to build an adaptive intelligent information retrieval system which will track web documents into similar topic using an unsupervised machine learning technique to reduce the percentage of irrelevant documents that are retrieved and presented to users. It was found that using Adaptive Neuro-Fuzzy Inference System improved the performance slightly by 0.22641 representing 22.64% over the Fuzzy Inference System(FIS) results thereby guarantee retrieval of most relevant information that meet the user’s request.

IMPLEMENTATION OF INTELLIGENT DOCUMENT RETREIVAL MODEL USING NEURO-FUZZY TECHNOLOGY