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

Journal of Information and Organizational Sciences

Mawloud Mosbah¹ 

[1] Informatics Department, Faculty of Sciences, University 20 Août 1955 of Skikda, Algeria;

关键词: Information Retrieval;    Google engine;    Query Expansion;    Query Reformulation;    Re-ranking;    Pseudo Relevance Feedback;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown