Search (6 results, page 1 of 1)

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Abstract

Search engines index only a proportion of the web and this proportion is not determined randomly but by following algorithms that take into account the properties that impact factors measure. A survey was conducted in order to test the coverage of search engines and to decide thether their partial coverage is indeed an obstacle to using them to calculate web impact factors. The results indicate that search engine coverage, even of large national domains is extremely uneven and would be likely to lead to misleading calculations

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a

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Abstract

Journal citation measures are one of the most widely used bibliometric tools. The most well-known measure is the ISI Impact Factor, under the standard definition, the impact factor of journal j in a given year is the average number of citations received by papers published in the previous two years of journal j. However, the impact factor has its "intrinsic" limitations, it is a ranking measure based fundamentally on a pure counting of the in-degrees of nodes in the network, and its calculation does not take into account the "impact" or "prestige" of the journals in which the citations appear. Google's PageRank algorithm and Kleinberg's HITS method are webpage ranking algorithm, they compute the scores of webpages based on a combination of the number of hyperlinks that point to the page and the status of pages that the hyperlinks originate from, a page is important if it is pointed to by other important pages. We demonstrate how popular webpage algorithm PageRank and HITS can be used ranking journal, and we compared ISI impact factor, PageRank and HITS for journal ranking, and with PageRank and HITS compute respectively including self-citation and non self-citation, and discussed the merit and shortcomings and the scope of application that the various algorithms are used to rank journal.

Type

a

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Abstract

Because of the increasing presence of scientific publications an the Web, combined with the existing difficulties in easily verifying and retrieving these publications, research an techniques and methods for retrieval of scientific Web publications is called for. In this article, we report an the initial steps taken toward the construction of a test collection of scientific Web publications within the subject domain of plant biology. The steps reported are those of data gathering and data analysis aiming at identifying characteristics of scientific Web publications. The data used in this article were generated based an specifically selected domain topics that are searched for in three publicly accessible search engines (Google, AlITheWeb, and AItaVista). A sample of the retrieved hits was analyzed with regard to how various publication attributes correlated with the scientific quality of the content and whether this information could be employed to harvest, filter, and rank Web publications. The attributes analyzed were inlinks, outlinks, bibliographic references, file format, language, search engine overlap, structural position (according to site structure), and the occurrence of various types of metadata. As could be expected, the ranked output differs between the three search engines. Apparently, this is caused by differences in ranking algorithms rather than the databases themselves. In fact, because scientific Web content in this subject domain receives few inlinks, both AItaVista and AlITheWeb retrieved a higher degree of accessible scientific content than Google. Because of the search engine cutoffs of accessible URLs, the feasibility of using search engine output for Web content analysis is also discussed.

Type

a

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Abstract

Search engines are normally used to find information or Web sites, but Webometric investigations use them for quantitative data such as the number of pages matching a query and the international spread of those pages. For this type of application, the accuracy of the hit count estimates and range of URLs in the full results are important. Here, we compare the applications programming interfaces of Google, Yahoo!, and Live Search for 1,587 single word searches. The hit count estimates were broadly consistent but with Yahoo! and Google, reporting 5-6 times more hits than Live Search. Yahoo! tended to return slightly more matching URLs than Google, with Live Search returning significantly fewer. Yahoo!'s result URLs included a significantly wider range of domains and sites than the other two, and there was little consistency between the three engines in the number of different domains. In contrast, the three engines were reasonably consistent in the number of different top-level domains represented in the result URLs, although Yahoo! tended to return the most. In conclusion, quantitative results from the three search engines are mostly consistent but with unexpected types of inconsistency that users should be aware of. Google is recommended for hit count estimates but Yahoo! is recommended for all other Webometric purposes.

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