Domain Specific Custom Search for Quicker Information Retrieval

Domain Specific Custom Search for Quicker Information Retrieval

Tushar Kanti Saha (Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh) and A. B. M. Shawkat Ali (School of Engineering and Technology, Central Queensland University, North Rockhampton, QLD, Australia & i-LaB Australia, North Sydney, NSW, Australia)
Copyright: © 2013 |Pages: 14
DOI: 10.4018/ijirr.2013070102
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Recently researchers are using Google scholar widely to find out the research articles and the relevant experts in their domain. But it is unable to find out all experts in a relevant research area from a specific country by a quick search. Basically the custom search technique is not available in the current Google scholar’s setup. The authors have combined custom search with domain-specific search and named as domain specific custom search in this research. First time this research introduces a domain specific custom search technique using new search methodology called n-paged-m-items partial crawling algorithm. This algorithm is a real-time faster crawling algorithm due to the partial crawling technique. It does not store anything in the database, which can be shown later on to the user. The proposed algorithm is implemented on a new domain to find out the scholars or experts quickly. Finally the authors observe the better performance of the proposed algorithm comparing with Google scholar.
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1. Introduction

Modern world is overwhelmed due to the huge amount of information. In general, knowledge seeker requires very specific information in a quick manner from the huge volume of information repository through the internet. Search engine is a common tool to meet the user demand. But when they post a query to the search engine, it returns thousand of results within a few seconds. User may get the desired result in the first line or in the first page of the search results. Otherwise, user has to look up page by page for finding the desired result. If desired result is not found within 4-5 pages, user usually tries another related query. Otherwise user moves to another search engine to meet the own demand. This is a time consuming process. Search engine service providers like Google, Yahoo, Bing, etc. have engaged their spiders to collect as many information as they can to enrich the repositories. These engines are also considered other issues, for instances page ranking, quick search result display, most relevant result display, etc. These are not focusing on user’s custom proper requirement. In this research we focus on that issue. Moreover, now-a-days custom search engine (Schmick, 2012) and domain-specific search (DSS) engines (Bhatt, 2003; Wikipedia, 2013a; Hanbury & Lupu, 2013) are very much popular among the people who are familiar with it. As a result we concentrate on combining these two search techniques called domain specific custom search (DSCS) technique on search engine results. In our work, domain has two different concepts. One is search area and another is website itself known as domain. Previous researchers worked on personalized DSS (Zhang, 2008) to retrieve data from Chinese domain. But we have personalized our search on single domain i.e. website and single area. Our DSCS technique works for expert retrieval depending on their research area and country. To discuss about DSCS tool for quicker information retrieval, we have taken expert retrieval as our custom search area and Google scholar domain as our work area.

The rest of the paper is ordered as follows. Section 2 discusses literary survey of domain specific custom search up to this year in different views. Section 3 and 4 show what DSCS is and why it is needed. Applications of domain specific custom search engine are shown in section 5. In section 6, current search trend at Google scholar and its pitfalls are discussed. As the proposed algorithm n-paged-m-items partial crawling is briefly discussed in section 7. A vast description of DSCS technique developed in this research is presented in section 8. Section 9 shows the proposed algorithm real life implementation as a tool. The tool performances are summarized in section 10. Future direction for the upcoming researchers is explained in section 11. The conclusion of this proposed research is summarised towards the end of this paper.

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