TY - GEN
T1 - Adaptively Intelligent Meta-search Engine with Minimum Edit Distance
AU - Kanwal, Asma
AU - Septyanto, Arif Wicaksono
AU - Muhammad, Muhammad Hassan Ghulam
AU - Said, Raed A.
AU - Farrukh, Muhammad
AU - Ibrahim, Muhammad
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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%.
AB - 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%.
KW - IMSE
KW - Meta-Search Engine
KW - Named Entity Recognizer
KW - Natural Language Processing
KW - Stemming
UR - https://www.scopus.com/pages/publications/85129590314
U2 - 10.1109/ICBATS54253.2022.9759088
DO - 10.1109/ICBATS54253.2022.9759088
M3 - Conference contribution
AN - SCOPUS:85129590314
T3 - 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022
BT - 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022
Y2 - 16 February 2022 through 17 February 2022
ER -