Search (280 results, page 2 of 14)

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Abstract

PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor alpha that spreads uniformly part of the rank. The choice of alpha is eminently empirical, and in most cases the original suggestion alpha=0.85 by Brin and Page is still used. Recently, however, the behaviour of PageRank with respect to changes in alpha was discovered to be useful in link-spam detection. Moreover, an analytical justification of the value chosen for alpha is still missing. In this paper, we give the first mathematical analysis of PageRank when alpha changes. In particular, we show that, contrarily to popular belief, for real-world graphs values of alpha close to 1 do not give a more meaningful ranking. Then, we give closed-form formulae for PageRank derivatives of any order, and an extension of the Power Method that approximates them with convergence O(t**k*alpha**t) for the k-th derivative. Finally, we show a tight connection between iterated computation and analytical behaviour by proving that the k-th iteration of the Power Method gives exactly the PageRank value obtained using a Maclaurin polynomial of degree k. The latter result paves the way towards the application of analytical methods to the study of PageRank.

Date

16. 1.2016 10:22:28

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a

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Abstract

This article presents a semantic-based Web retrieval system that is capable of retrieving the Web pages that are conceptually related to the implicit concepts of the query. The concept of concept is managed from a fuzzy point of view by means of semantic areas. In this context, the proposed system improves most search engines that are based on matching words. The key of the system is to use a new version of the Fuzzy Interrelations and Synonymy-Based Concept Representation Model (FIS-CRM) to extract and represent the concepts contained in both the Web pages and the user query. This model, which was integrated into other tools such as the Fuzzy Interrelations and Synonymy based Searcher (FISS) metasearcher and the fz-mail system, considers the fuzzy synonymy and the fuzzy generality interrelations as a means of representing word interrelations (stored in a fuzzy synonymy dictionary and ontologies). The new version of the model, which is based on the study of the cooccurrences of synonyms, integrates a soft method for disambiguating word senses. This method also considers the context of the word to be disambiguated and the thematic ontologies and sets of synonyms stored in the dictionary.

Date

22. 7.2006 17:14:12

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Abstract

One finding of the 2006 OCLC study of College Students' Perceptions of Libraries and Information Resources was that students expressed equal levels of trust in libraries and search engines when it came to meeting their information needs in a way that they felt was authoritative. Seeking to incorporate this insight into our own instructional methodology, Schaffer Library at Union College has attempted to engineer a shift from Google to Google Scholar among our student users by representing Scholar as a viable adjunct to the catalog and to snore traditional electronic resources. By attempting to engage student researchers on their own terms, we have discovered that most of them react enthusiastically to the revelation that the Google they think they know so well is, it turns out, a multifaceted resource that is capable of delivering the sort of scholarly information that will meet with their professors' approval. Specifically, this article focuses on the fact that many Google Scholar searches link hack to our own Web catalog where they identify useful book titles that direct OPAC keyword searches have missed.

Date

2.12.2007 19:39:22

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a

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Abstract

The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent-based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar-pages searching mode. Our agent-based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity-oriented fuzzy clustering, and knowledge-based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.

Date

22. 7.2006 16:59:13

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a

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Abstract

This paper presents an application of the model described in Part I to the evaluation of Web search engines by undergraduates. The study observed how 36 undergraduate used four major search engines to find information for their own individual problems and how they evaluated these engines based an actual interaction with the search engines. User evaluation was based an 16 performance measures representing five evaluation criteria: relevance, efficiency, utility, user satisfaction, and connectivity. Non-performance (user-related) measures were also applied. Each participant searched his/ her own topic an all four engines and provided satisfaction ratings for system features and interaction and reasons for satisfaction. Each also made relevance judgements of retrieved items in relation to his/her own information need and participated in post-search Interviews to provide reactions to the search results and overall performance. The study found significant differences in precision PR1 relative recall, user satisfaction with output display, time saving, value of search results, and overall performance among the four engines and also significant engine by discipline interactions an all these measures. In addition, the study found significant differences in user satisfaction with response time among four engines, and significant engine by discipline interaction in user satisfaction with search interface. None of the four search engines dominated in every aspect of the multidimensional evaluation. Content analysis of verbal data identified a number of user criteria and users evaluative comments based an these criteria. Results from both quantitative analysis and content analysis provide insight for system design and development, and useful feedback an strengths and weaknesses of search engines for system improvement

Date

24. 1.2004 18:27:22

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Abstract

Surfing the World Wide Web used to be cool, dude, real cool. But things have gotten hot - so hot that finding something useful an the Web is no longer cool. It is suffocating Web searchers in the smoke and debris of mountain-sized lists of hits, decisions about which search engines they should use, whether they will get lost in the dizzying maze of a subject directory, use the right syntax for the search engine at hand, enter keywords that are likely to retrieve hits an the topics they have in mind, or enlist a browser that has sufficient functionality to display the most promising hits. When it comes to Web searching, in a few short years we have gone from the cool image of surfing the Web into the frying pan of searching the Web. We can turn down the heat by rethinking what Web searchers are doing and introduce some order into the chaos. Web search strategies that are tool-based-oriented to specific Web searching tools such as search en gines, subject directories, and meta search engines-have been widely promoted, and these strategies are just not working. It is time to dissect what Web searching tools expect from searchers and adjust our search strategies to these new tools. This discussion offers Web searchers help in the form of search strategies that are based an strategies that librarians have been using for a long time to search commercial information retrieval systems like Dialog, NEXIS, Wilsonline, FirstSearch, and Data-Star.

Date

22. 9.1997 19:16:05

Type

a

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Abstract

The World Wide Web is a popular and interactive medium used to collect, disseminate, and access an increasingly huge amount of information, which constitutes the mainstay of the so-called information and knowledge society. Because of its spectacular growth, related to both Web resources (pages, sites, and services) and number of users, the Web is nowadays the main information repository and provides some automatic systems for locating, accessing, and retrieving information. However, an open and crucial question remains: how to provide fast and effective retrieval of the information relevant to specific users' needs. This is a very hard and complex task, since it is pervaded with subjectivity, vagueness, and uncertainty. The expression soft computing refers to techniques and methodologies that work synergistically with the aim of providing flexible information processing tolerant of imprecision, vagueness, partial truth, and approximation. So, soft computing represents a good candidate to design effective systems for information access and retrieval on the Web. One of the most representative tools of soft computing is fuzzy set theory. This special topic section collects research articles witnessing some recent advances in improving the processes of information access and retrieval on the Web by using soft computing tools, and in particular, by using fuzzy sets and/or integrating them with other soft computing tools. In this introductory article, we first review the problem of Web retrieval and the concept of soft computing technology. We then briefly introduce the articles in this section and conclude by highlighting some future research directions that could benefit from the use of soft computing technologies.

Date

22. 7.2006 16:59:33

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Abstract

Social phenomena happen, and the historians follow. So it goes with Google, the latest star shooting through the universe of trend-setting businesses. This company has even entered our popular lexicon: as many note, "Google" has moved beyond noun to verb, becoming an action which most tech-savvy citizens at the turn of the twenty-first century recognize and in fact do, on a daily basis. It's this wide societal impact that fascinated authors David Vise and Mark Malseed, who came to the book with well-established reputations in investigative reporting. Vise authored the bestselling The Bureau and the Mole, and Malseed contributed significantly to two Bob Woodward books, Bush at War and Plan of Attack. The kind of voluminous research and behind-the-scenes insight in which both writers specialize, and on which their earlier books rested, comes through in The Google Story. The strength of the book comes from its command of many small details, and its focus on the human side of the Google story, as opposed to the merely academic one. Some may prefer a dryer, more analytic approach to Google's impact on the Internet, like The Search or books that tilt more heavily towards bits and bytes on the spectrum between technology and business, like The Singularity is Near. Those wanting to understand the motivations and personal growth of founders Larry Page and Sergey Brin and CEO Eric Schmidt, however, will enjoy this book. Vise and Malseed interviewed over 150 people, including numerous Google employees, Wall Street analysts, Stanford professors, venture capitalists, even Larry Page's Cub Scout leader, and their comprehensiveness shows. As the narrative unfolds, readers learn how Google grew out of the intellectually fertile and not particularly directed friendship between Page and Brin; how the founders attempted to peddle early versions of their search technology to different Silicon Valley firms for $1 million; how Larry and Sergey celebrated their first investor's check with breakfast at Burger King; how the pair initially housed their company in a Palo Alto office, then eventually moved to a futuristic campus dubbed the "Googleplex"; how the company found its financial footing through keyword-targeted Web ads; how various products like Google News, Froogle, and others were cooked up by an inventive staff; how Brin and Page proved their mettle as tough businessmen through negotiations with AOL Europe and their controversial IPO process, among other instances; and how the company's vision for itself continues to grow, such as geographic expansion to China and cooperation with Craig Venter on the Human Genome Project. Like the company it profiles, The Google Story is a bit of a wild ride, and fun, too. Its first appendix lists 23 "tips" which readers can use to get more utility out of Google. The second contains the intelligence test which Google Research offers to prospective job applicants, and shows the sometimes zany methods of this most unusual business. Through it all, Vise and Malseed synthesize a variety of fascinating anecdotes and speculation about Google, and readers seeking a first draft of the history of the company will enjoy an easy read.

Date

3. 5.1997 8:44:22

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Abstract

This study introduces methods for evaluating search engine performance over a time period. Several measures are defined, which as a whole describe search engine functionality over time. The necessary setup for such studies is described, and the use of these measures is illustrated through a specific example. The set of measures introduced here may serve as a guideline for the search engines for testing and improving their functionality. We recommend setting up a standard suite of measures for evaluating search engine performance.

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Abstract

Waters and Amoudi describe GeoSearcher, a prototype ranking program that arranges search engine results along a geo-spatial dimension without the provision of geo-spatial meta-tags or the use of geo-spatial feature extraction. GeoSearcher uses URL analysis, IptoLL, Whois, and the Getty Thesaurus of Geographic Names to determine site location. It accepts the first 200 sites returned by a search engine, identifies the coordinates, calculates their distance from a reference point and ranks in ascending order by this value. For any retrieved site the system checks if it has already been located in the current session, then sends the domain name to Whois to generate a return of a two letter country code and an area code. With no success the name is stripped one level and resent. If this fails the top level domain is tested for being a country code. Any remaining unmatched names go to IptoLL. Distance is calculated using the center point of the geographic area and a provided reference location. A test run on a set of 100 URLs from a search was successful in locating 90 sites. Eighty three pages could be manually found and 68 had sufficient information to verify location determination. Of these 65 ( 95%) had been assigned reasonably correct geographic locations. A random set of URLs used instead of a search result, yielded 80% success.

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Abstract

This paper deals with the measurement of bias in search engines on the World Wide Web. Bias is taken to mean the balance and representativeness of items in a collection retrieved from a database for a set of queries. This calls for assessing the degree to which the distribution of items in a collection deviates from the ideal. Ascertaining this ideal poses problems similar to those associated with determining relevance in the measurement of recall and precision. Instead of enlisting subject experts or users to determine such an ideal, a family of comparable search engines is used to approximate it for a set of queries. The distribution is obtained by computing the frequencies of occurrence of the uniform resource locators (URLs) in the collection retrieved by several search engines for the given queries. Bias is assessed by measuring the deviation from the ideal of the distribution produced by a particular search engine.

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Abstract

This paper discusses the impact of metadata implementation in a webpage on its visibility performance in a search engine results list. Influential internal and external factors of metadata implementation were identified. How these factors affect webpage visibility in a search engine results list was examined in an experimental study. Findings suggest that metadata is a good mechanism to improve webpage visibility, the metadata subject field plays a more important role than any other metadata field and keywords extracted from the webpage itself, particularly title or full-text, are most effective. To maximize the effects, these keywords should come from both title and full-text.

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Abstract

This paper discusses the impact of metadata implementation in a webpage on its visibility performance in a search engine results list. Influential internal and external factors of metadata implementation were identified. How these factors affect webpage visibility in a search engine results list was examined in an experimental study. Findings suggest that metadata is a good mechanism to improve webpage visibility, the metadata subject field plays a more important role than any other metadata field and keywords extracted from the webpage itself, particularly title or full-text, are most effective. To maximize the effects, these keywords should come from both title and full-text.

Type

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Abstract

The basic steps to build one are fairly simple: * Sign up * Pick a search engine name * Choose a list of sites * Add the sites * Publish That quickly, a search engine can be created to search a specific portion of the web, such as local government sites, childcare resources, or historical archives. It is easy to create a simple customized vertical search engine as well as support much more advanced capabilities (see the Google AJAX search API article). Try these tools and tips and build a customized search engine or two for your own users to help them find more targeted and relevant web information.

Type

a

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Abstract

The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.

Type

a