Search results

This paper evaluates the precision of four metasearch engines (MSEs) – DuckDuckGo, Dogpile, Metacrawler and Startpage, to determine which metasearch engine exhibits the highest level of precision and to identify the metasearch engine that is most likely to return the most relevant search results.

The research is divided into two parts: the first phase involves four queries categorized into two segments (4-Q-2-S), while the second phase includes six queries divided into three segments (6-Q-3-S). These queries vary in complexity, falling into three types: simple, phrase and complex. The precision, average precision and the presence of duplicates across all the evaluated metasearch engines are determined.

The study clearly demonstrated that Startpage returned the most relevant results and achieved the highest precision (0.98) among the four MSEs. Conversely, DuckDuckGo exhibited consistent performance across both phases of the study.

The study only evaluated four metasearch engines, which may not be representative of all available metasearch engines. Additionally, a limited number of queries were used, which may not be sufficient to generalize the findings to all types of queries.

The findings of this study can be valuable for accreditation agencies in managing duplicates, improving their search capabilities and obtaining more relevant and precise results. These findings can also assist users in selecting the best metasearch engine based on precision rather than interface.

The study is the first of its kind which evaluates the four metasearch engines. No similar study has been conducted in the past to measure the performance of metasearch engines.

The purpose of this paper is to introduce two new automatic methods for evaluating the performance of search engines. The reported study uses the methods to experimentally investigate which search engine among three popular search engines (Ask.com, Bing and Google) gives the best performance.

The study assesses the performance of three search engines. For each one the weighted average of similarity degrees between its ranked result list and those of its metasearch engines is measured. Next these measures are compared to establish which search engine gives the best performance. To compute the similarity degree between the lists two measures called the “tendency degree” and “coverage degree” are introduced; the former assesses a search engine in terms of results presentation and the latter evaluates it in terms of retrieval effectiveness. The performance of the search engines is experimentally assessed based on the 50 topics of the 2002 TREC web track. The effectiveness of the methods is also compared with human‐based ones.

Google outperformed the others, followed by Bing and Ask.com. Moreover significant degrees of consistency – 92.87 percent and 91.93 percent – were found between automatic and human‐based approaches.

The findings of this work could help users to select a truly effective search engine. The results also provide motivation for the vendors of web search engines to improve their technology.

The paper focuses on two novel automatic methods to evaluate the performance of search engines and provides valuable experimental results on three popular ones.

This paper seeks to disclose the important role of missing documents, broken links and duplicate items in the results merging process of a metasearch engine in detail. It aims to investigate some related practical challenges and proposes some solutions. The study also aims to employ these solutions to improve an existing model for results aggregation.

This research measures the amount of an increase in retrieval effectiveness of an existing results merging model that is obtained as a result of the proposed improvements. The 50 queries of the 2002 TREC web track were employed as a standard test collection based on a snapshot of the worldwide web to explore and evaluate the retrieval effectiveness of the suggested method. Three popular web search engines (Ask, Bing and Google) as the underlying resources of metasearch engines were selected. Each of the 50 queries was passed to all three search engines. For each query the top ten non‐sponsored results of each search engine were retrieved. The returned result lists of the search engines were aggregated using a proposed algorithm that takes the practical issues of the process into consideration. The effectiveness of the result lists generated was measured using a well‐known performance indicator called “TSAP” (TREC‐style average precision).

Experimental results demonstrate that the proposed model increases the performance of an existing results merging system by 14.39 percent on average.

The findings of this research would be helpful for metasearch engine designers as well as providing motivation to the vendors of web search engines to improve their technology.

This study provides some valuable concepts, practical challenges, solutions and experimental results in the field of web metasearching that have not been previously investigated.

This study evaluates the retrieval of New Zealand information using three local New Zealand search engines, four major global search engines and three metasearch engines. Searches for NZ topics were carried out on all the search engines, and the relative recall calculated. The local search engines did not achieve higher recall than the global search engines or metasearch engines, but no search engine achieved more than 45 percent recall. Despite the theoretical advantage of searching the databases of several individual search engines, metasearch engines did not achieve higher recall. Of relevant pages for the queries, 36 percent were outside the .nz domain. Implications for searching for geographically specific information, and for evaluation of search engines, are discussed.

The demand for metasearch capabilities – which enable users to simultaneously search heterogeneous information resources – is constantly increasing in the scholarly information environment as the number of available resources grows. To make efficient and accurate metasearching possible, library technology has begun to address several issues. First, information about resources must be accessible to metasearch systems. Such information, called resource metadata, can be made available to metasearch systems in various ways. Second, a metasearch system must be able to convert a unified query as necessary and adapt it to the requirements of each searched resource, retrieve the results, and display them to the end‐user in a comprehensive and friendly manner. Finally, because some repositories are not available to metasearch systems, local indexes can be created to access them. The MetaLib library portal from Ex Libris is used to provide examples where relevant.

To describe new features in ARTstor.

The piece includes descriptions of the new functionality – an XML gateway that will enable federated searches initiated locally via a metasearch engine to include results from the ARTstor Digital Library; and the development of a standard application programming interface (API) that will allow content stored in local repositories or other licensed resources to be searchable and retrievable via the ARTstor software.

ARTstor Digital Library is a repository of hundreds of thousands of digital images and related data; the tools to use those images; and a usage environment that seeks to balance the rights of content providers with the needs and interests of content users.

Provides information of value to information management professionals.

This paper explores resource discovery issues relating to New Zealand/Aotearoa information on the WWW in the twenty‐first century. Questions addressed are: How do New Zealand search engines compare with global search engines for finding information relating to New Zealand? Can search engines find everything that is available on the web? What are effective strategies for finding information relating to New Zealand on the web? What is the quality of NZ information on the web? What can librarians do to make NZ information more accessible on the web? Based on a study, it concludes that neither local nor global search engines are by themselves sufficient, and that to maximize retrieval a variety of engines is necessary. The NZ librarian can play a role in ensuring that NZ information is made both available and accessible. Although the paper discusses the situation in New Zealand, the results and conclusions are applicable to other countries.

The purpose of this paper is to present a new method for evaluating the performance of metasearch engines (MSEs), which was used in the reported study to investigate which of eight popular MSEs (Clusty, Dogpile, Excite, Mamma, MetaCrawler, Search.com, WebCrawler and Webfetch) is the best.

This research evaluated the performance of eight MSEs. For each MSE the average of closeness degrees between its ranked result list and those of its underlying search engines (SEs) was measured. Next, these measures were compared to each other to determine which MSE gives the best performance. Furthermore the experiment was repeated ten times with ten different queries to reach a stable result.

The findings revealed that Dogpile outperformed all the others, followed by MetaCrawler, Excite, Webfetch and then Mamma. MetaCrawler and WebCrawler had almost the same performance and occupied the next positions. Clusty and Search.com performed poorly in comparison to the others.

The findings of this research would be useful for MSE designers as well as helping the numerous users of MSEs to choose a truly effective one.

This paper provides a novel method for assessing the performance of MSEs and valuable experimental results on eight popular ones.

MetaOPAC Azalai Italiano (MAI) is a virtual union catalogue of Italian libraries developed through co‐operation between the Italian Library Association and the Consorzio Interuniversitario Lombardo per l'Elaborazione Automatica. This paper presents the components of MetaOPAC Azalai Italiano and the organisational, management, planning and implementation tools developed by the team since 1999. MAI provides access to Italian OPACs, offering a directory and metasearch functionality. The search engine, Azalai, performs metasearching. The architecture of the system, the search engine and converter, is based on a database of Italian OPACs. Three different interfaces, designed for specific types of users, provide access to the system. Members of the MAI Editorial Board are responsible for keeping the database updated and this automatically generates the Directory of Italian Online Catalogues. MAI is divided into five distinct sections, integrated with a range of tools and services intended for different categories of user.

The purpose was to evaluate the effectiveness of Google (as an international search engine) as well as of Parsijoo, Rismoon, and Yooz (as Persian search engines).

In this research, Google search engine as an international search engine, and three local ones, Parsijoo, Rismoon, and Yooz, were selected for evaluation. Likewise, 32 subject headings were selected from the Persian Subject Headings List, and then simulated work tasks were assigned based on them. A total of 192 students from Ferdowsi University of Mashhad were asked to search for the information needed for simulated work tasks in the selected search engines, and then to copy the relevant website URLs in the search form.

The findings indicated that Google, Parsijoo, Rismoon, and Yooz had a significant difference in the precision, recall, and normalized discounted cumulative gain. There was also a significant difference in the effectiveness (average of precision, recall, and NDCG) of these four search engines in the retrieval of the Persian resources.

Users using an efficient search engine will attain more relevant documents, and Google search engine was more efficient in retrieving the Persian resources. It is recommended to use Google as it has a more efficient search.

In this research, for the first time, Google has been compared with local Persian search engines considering the new approach (simulated work tasks).