Intelligent rule-based approach for effective information retrieval and dynamic storage in local repositories

Rules in artificial intelligence are able to provide inference facilities for making effective decisions in information retrieval. In such a scenario, a keyword-based information retrieval systems use the keywords as indexes generated by Web crawlers without considering semantics. Moreover, many Web search engines perform re-ranking based on relevance feedback. However, the relevant documents in e-learning applications must be stored in local repositories and must be updated dynamically based on the level of the users. Therefore, it is necessary to have an information retrieval system which performs relevant information extraction and stores such relevant information dynamically in local e-learning repositories. In order to address these issues, a new information retrieval and local storage system is proposed in this paper by applying rules for making effective decisions in the storage and retrieval algorithms which are newly proposed in this paper. For this purpose, two new algorithms called intelligent rule-based relevant information retrieval algorithm with semantics and a secured information storage using semantic knowledge representation are proposed in this paper for effectively retrieving the e-learning contents from the Web on computer science subject and to store them in local repositories with semantic indexing. The major advantages of the proposed information retrieval system include increase in accuracy, reduction in retrieval time and effective storage in local repositories for further use.