Improving Domain Searches through Customized Search Engines

International Journal of Information Retrieval Research, 2013

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 scholar.google.com to find out the scholars or experts quickly. Finally the authors observe the better performance of the proposed algorithm ...

Traditional web which is the largest information database lacks semantic and as a result the information available in the web is only human understandable, not by machine. With the rapid increase in the amount of information on networks, search engine has become the infrastructure for people gaining access to Web information, and is the second largest Internet application besides e-mail. However, search engine returns a huge number of results, and the relevance between results and user queries is also different. There are lots of search engines available today, but the way to retrieve meaningful information is difficult. To overcome this problem in search engines to retrieve meaningful information intelligently or smartly, Semantic Web technology has played a major role. In the light of this, our paper, proposes an algorithm, architecture for the semantic web based search engine named EWEBSEARCH model, powered by XML meta-tags (which ensures machine understandability) to improve web search. The EWEBSEARCH model provides a simple interface to capture user's queries (keywords), then the search or query engine processes the queries from the repository (database) using the search engine algorithm, interpreting the queries, retrieving and providing appropriate ranking of results in order to satisfy users queries. Query answers are ranked using extended information-retrieval techniques, are generated in an order of ranking and implementation of the model.

Information Retrieval, 2006

When searching for health information, results quality can be judged against available scientific evidence: Do search engines return advice consistent with evidence based medicine? We compared the performance of domain-specific health and depression search engines against a general-purpose engine (Google) on both relevance of results and quality of advice. Over 101 queries, to which the term 'depression' was added if not already present, Google returned more relevant results than those of the domain-specific engines. However, over the 50 treatment-related queries, Google returned 70 pages recommending for or against a well studied treatment, of which 19 strongly disagreed with the scientific evidence. A domain-specific index of 4 sites selected by domain experts was only wrong in 5 of 50 recommendations. Analysis suggests a tension between relevance and quality. Indexing more pages can give a greater number of relevant results, but selective inclusion can give better quality.

The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023

Search engines play a vital role in information retrieval (IR) indexing and processing vast and diverse data, which now encompasses the ever-expanding wealth of multimedia content. However, search engine performance relies on the efficiency and effectiveness of their information retrieval systems (IRS). To enhance search engine performance, there is a need to develop more efficient and accurate IRS that retrieves relevant information quickly and accurately. To address this challenge, various approaches, including inverted indexing, query expansion, and relevance feedback, have been proposed for IR. Although these approaches have shown promising results, but their effectiveness and limitations require a comprehensive examination This research aims to investigate the challenges and opportunities in designing an efficient IRS for search engines and identify key areas for improvement and future research. The study involves a comprehensive literature review on IR impacting academia, industry, healthcare, e-commerce, and other domains. Researchers rely on search engines to access relevant scientific papers, professionals use them to gather market intelligence, and consumers utilize them for product research and decision-making. The findings of this study will contribute to the development of more efficient and effective IRS, leading to improved search engine performance and user satisfaction.

In order to solve the problem of information overkill on the web or large domains, current information retrieval tools especially search engines need to be improved. Much more intelligence should be embedded to search tools to manage the search and filtering processes effectively and present relevant information. As the web swells with more and more data, the predominant way of sifting through all of that data -keyword search -will one day break down in its ability to deliver the exact information people want at our fingertips. Hence search engines are trying to break the shackles of the concept of keyword search what typically most search engines do. This paper tries to identify the major challenges for today's keyword search engines to adapt with the fast growth of web and support comprehensive user demands in quick time. Then it surveys different non-keyword based paradigms proposed, developed or implemented by researchers and different search engines and also classifies those approaches according to the features focused by the different search engines to deliver results.

2023

International Journal of Information Processing and Management, 2013

The explosive growth of data available in the internet exposes a serious problem, informationoverflow, where each user gets rarely necessary information and which brings blind spot of information search. The blind spot means the areas which cannot be accessed by search engines. Hence, there is no way users can get the information in blind spots. They are getting wider, which cause loss of valuable information for users' queries. The problem of blind spots stems from the way of navigating the web for current leading search engines, Google or Yahoo; they crawl web pages periodically and automatically, store them into indexed databases, and retrieve search results via queries. However, the rapid growth of the web data brings a limit of indexing pages, which massproduces data areas that cannot be accessed by the search engines. Besides, they still retrieve useless results for depending on a few keywords, where users wander again for really necessary information. The truly required searching way is to provide valuable and accurate search results to users in a customized way and to deliver the information from the viewpoint of a user, not from the viewpoint of a search engine provider. Recently, fresh search engines are developed and issued with Silicon Valley as the center. Their objectives are the intelligent and specialized search results as well as easy user interfaces. In this manuscript, we introduce some representatives of the newly published search engines along with surveying and classifying systematically current existing web search engines.

2019

The amount of information stored in the Web is huge and without help from search engines it becomes almost impossible to find what you want. The search engines try to index as many web pages as possible. On April 3 rd 2018 there were over 45.5 billion webpages indexed by Google and just over 4 billion web indexed by Bing. Even with the help of search engines, the number of addresses of pages returned by them for a simple query is quite large and we, as people, must look through those pages to find what we are really interested in. Finding the URLs in such a huge data space is challenging and requires some special techniques for refining and reducing the number of returned addresses. In our paper we present some interrogation modifiers and search operators used by the Google search engine to reduce significantly the number of the search results and to increase the quality of that results. The presented techniques and commands for search engines that can be used for finding similar we...

International Journal of Advances in Scientific Research and Engineering (ijasre), 2019

Users need more effective ways of organizing and searching for information. It has become increasingly difficult for users to find information on the World Wide Web that satisfies their individual needs since information resources on the World Wide Web continue to grow. The reason is that the World Wide Web provides a bunch of information that is generic in perspective. Although, the generic search engine makes attempts to streamline searching to search-key by traversing and combing the whole bunch of stored information. Under these circumstances, the system turns out to be inefficient and many times the results do not tally to what users desire. This paper presents a system to find interesting textual content among tons of documents taking the department of computer science of the University of Abuja, Nigeria as a case study. The paper proposed a vector space ranking algorithm which is a content-based ranking method that allows the user to utilize the full co-occurrence matrix of all words in the corpus to bring out relevant material using a simple and structured query interface. The index structures of the system have been specifically designed to support the ranking scheme. One important aspect of the system is its flexibility and robustness that makes it adaptable to any domain rather than being tailored to a particular one.