Metasearch Engine: A Technology for Information Extraction in Knowledge Computing

Vijaya, P. A.; Chander, Satish

doi:10.1007/978-981-10-8258-0_10

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Search engines provide direct access to relevant web pages. In contrast, metasearch engines offer a consolidated view of results from various search engines, potentially saving time but relying on the accuracy of those engines (Vijaya and Chander, 2018). Meta-search engines are designed to use keywords and semantic queries to maximize efficiency and minimize time waste (Kanwal et al, 2022).…”

Section: Ses Explosion Eramentioning

confidence: 99%

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Bhardwaj,

Kumar,

Nazim

2024

PMM

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PurposeThis paper evaluates the precision of four metasearch engines (MSEs) – DuckDuckGo, Dogpile, Metacrawler and Startpage, to determine which metasearch engine exhibits the highest level of precision and to identify the metasearch engine that is most likely to return the most relevant search results.Design/methodology/approachThe research is divided into two parts: the first phase involves four queries categorized into two segments (4-Q-2-S), while the second phase includes six queries divided into three segments (6-Q-3-S). These queries vary in complexity, falling into three types: simple, phrase and complex. The precision, average precision and the presence of duplicates across all the evaluated metasearch engines are determined.FindingsThe study clearly demonstrated that Startpage returned the most relevant results and achieved the highest precision (0.98) among the four MSEs. Conversely, DuckDuckGo exhibited consistent performance across both phases of the study.Research limitations/implicationsThe study only evaluated four metasearch engines, which may not be representative of all available metasearch engines. Additionally, a limited number of queries were used, which may not be sufficient to generalize the findings to all types of queries.Practical implicationsThe findings of this study can be valuable for accreditation agencies in managing duplicates, improving their search capabilities and obtaining more relevant and precise results. These findings can also assist users in selecting the best metasearch engine based on precision rather than interface.Originality/valueThe study is the first of its kind which evaluates the four metasearch engines. No similar study has been conducted in the past to measure the performance of metasearch engines.

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Section: Ses Explosion Eramentioning

confidence: 99%

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Bhardwaj,

Kumar,

Nazim

2024

PMM

View full text Add to dashboard Cite

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RETRACTED ARTICLE: Efficiently harvesting deep web interfaces based on adaptive learning using two-phase data crawler framework

Murugudu

Reddy

2021

Soft Comput

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Rider-Rank Algorithm-Based Feature Extraction for Re-ranking the Webpages in the Search Engine

Sankpal

Patil

2020

The Computer Journal

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The webpage re-ranking is a challenging task while retrieving the webpages based on the query of the user. Even though the webpages in the search engines are ordered depends on the importance of the content, retrieving the necessary documents based on the input query is quite difficult. Hence, it is required to re-rank the webpages available in the websites based on the features of the pages in the search engines, like Google and Bing. Thus, an effective Rider-Rank algorithm is proposed to re-rank the webpages based on the Rider Optimization Algorithm (ROA). The input queries are forwarded to different search engines, and the webpages generated from the search engines with respect to the input query are gathered. Initially, the keywords are generated for the webpages. Then, the top keyword is selected, and the features are extracted from the top keyword using factor-based, text-based and rank-based features of the webpage. Finally, the webpages are re-ranked using the Rider-Rank algorithm. The performance of the proposed approach is analyzed based on the metrics, such as F-measure, recall and precision. From the analysis, it can be shown that the proposed algorithm obtains the F-measure, recall and precision of 0.90, 0.98 and 0.84, respectively.

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Metasearch Engine: A Technology for Information Extraction in Knowledge Computing

Cited by 5 publications

References 13 publications

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

RETRACTED ARTICLE: Efficiently harvesting deep web interfaces based on adaptive learning using two-phase data crawler framework

Rider-Rank Algorithm-Based Feature Extraction for Re-ranking the Webpages in the Search Engine

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