Improving the Results of Google Scholar Engine through Automatic Query Expansion Mechanism and Pseudo Re-ranking using MVRA
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...
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| Format: | Article |
| Language: | English |
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University of Zagreb, Faculty of organization and informatics
2018-12-01
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| Series: | Journal of Information and Organizational Sciences |
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| Online Access: | https://jios.foi.hr/index.php/jios/article/view/1123 |
| Summary: | 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. |
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| ISSN: | 1846-3312 1846-9418 |