Improving the Results of Google Scholar Engine through Automatic Query Expansion Mechanism and Pseudo Re-ranking using MVRA

Autor:	Mawloud Mosbah
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Information Retrieval Google engine Query Expansion Query Reformulation Re-ranking Pseudo Relevance Feedback MVRA. Information theory Q350-390
Zdroj:	Journal of Information and Organizational Sciences, Vol 42, Iss 2 (2018)
Druh dokumentu:	article
ISSN:	1846-3312 1846-9418
DOI:	10.31341/jios.42.2.5
Popis:	In this paper, we address the enhancing of Google Scholar engine, in the context of text retrieval, through two mechanisms related to the interrogation protocol of that query expansion and reformulation. The both schemes are applied with re-ranking results using a pseudo relevance feedback algorithm that we have proposed previously in the context of Content based Image Retrieval (CBIR) namely Majority Voting Re-ranking Algorithm (MVRA). The experiments conducted using ten queries reveal very promising results in terms of effectiveness.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/74c68760bb714ae798ccad40eb588f22 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF