Adaptively Intelligent Meta-search Engine with Minimum Edit Distance
Institute of Electrical and Electronics Engineers Inc.
In current era retrieval of information has attained high demand due to spectra from websites has abundantly increased. Search Engines are basic tools to get information from the web of data and show irrelevant data causing wastage of time. Considering the fact that time is a precious commodity and is the hallmark of everything around us. To overcome the wastage of time and for its optimum utilization meta-search engines are design. Meta-Search Engine use to fetch relevant data. Existing meta-search engine shows their relevant data based on keywords as well as a semantic query. Semantic query-based results still have some irrelevancy in the results. In this paper, we analyze the semantic query based on machine learning algorithms. This paper hypothesizes improved results through the query expansion mechanism. Author also remove duplicated URLs that come from multiple search engines. Minimum Edit Distance algorithm is used to measure the similarity between titles, snippets and if measuring similarity is more than 0.6 then it must remove that title and snippet. Ranking process, generated retrieval of the relevant document at the top relevant document. Comparative analysis of proposed work is done with existing meta-search engines, overall performance of Intelligent Meta-Search Engine (IMSE) remains 74.17%. © 2022 IEEE.
This conference paper is not available at CUD collection. The version of scholarly record of this paper is published in 2022 International Conference on Business Analytics for Technology and Security (ICBATS) (2022), available online at: https://doi.org/10.1109/ICBATS54253.2022.9759088
IMSE, Meta-Search Engine, Named Entity Recognizer, Natural Language Processing, Stemming
Kanwal, A., Septyanto, A. W., Muhammad, M. H. G., Said, R. A., Farrukh, M., & Ibrahim, M. (2022). Adaptively intelligent meta-search engine with minimum edit distance. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). https://doi.org/10.1109/ICBATS54253.2022.9759088