Search (41 results, page 2 of 3)

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 940) [ClassicSimilarity], result of:
      0.014565565 = score(doc=940,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 940, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=940)
  0.25 = coord(1/4)

Content

Enthält eine Zusammenstellung der Werkzeuge und Hilfsmittel des Information Retrieval

Source

Information Research. 5(2000) no.3, April 2000

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 4437) [ClassicSimilarity], result of:
      0.014565565 = score(doc=4437,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 4437, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4437)
  0.25 = coord(1/4)

Abstract

This site provides a collection of schemas, i.e., html tags, that webmasters can use to markup their pages in ways recognized by major search providers. Search engines including Bing, Google and Yahoo! rely on this markup to improve the display of search results, making it easier for people to find the right web pages. Many sites are generated from structured data, which is often stored in databases. When this data is formatted into HTML, it becomes very difficult to recover the original structured data. Many applications, especially search engines, can benefit greatly from direct access to this structured data. On-page markup enables search engines to understand the information on web pages and provide richer search results in order to make it easier for users to find relevant information on the web. Markup can also enable new tools and applications that make use of the structure. A shared markup vocabulary makes easier for webmasters to decide on a markup schema and get the maximum benefit for their efforts. So, in the spirit of sitemaps.org, Bing, Google and Yahoo! have come together to provide a shared collection of schemas that webmasters can use.

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 4704) [ClassicSimilarity], result of:
      0.014565565 = score(doc=4704,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 4704, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4704)
  0.25 = coord(1/4)

Abstract

Swoogle is a crawler-based indexing and retrieval system for the Semantic Web, i.e., for Web documents in RDF or OWL. It extracts metadata for each discovered document, and computes relations between documents. Discovered documents are also indexed by an information retrieval system which can use either character N-Gram or URIrefs as keywords to find relevant documents and to compute the similarity among a set of documents. One of the interesting properties we compute is rank, a measure of the importance of a Semantic Web document.

Source

CIKM '04 Proceedings of the thirteenth ACM international conference on Information and knowledge management

0.003538796 = product of:
  0.014155184 = sum of:
    0.014155184 = weight(_text_:information in 1555) [ClassicSimilarity], result of:
      0.014155184 = score(doc=1555,freq=34.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.15993917 = fieldWeight in 1555, product of:
          5.8309517 = tf(freq=34.0), with freq of:
            34.0 = termFreq=34.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.015625 = fieldNorm(doc=1555)
  0.25 = coord(1/4)

Content

"Introduction Yahoo! is the undisputed king of the Web directories, providing one of the key information navigation tools on the Internet. It has maintained its popularity over many Internet-years as the most visited Web site, against intense competition. This is because it does a good job of shifting, cataloguing and organising the Web [1] . But what would a map of Yahoo!'s hierarchical classification of the Web look like? Would an interactive map of Yahoo!, rather than the conventional listing of sites, be more useful as navigational tool? We can get some idea what a map of Yahoo! might be like by taking a look at ET-Map, a prototype developed by Hsinchun Chen and colleagues in the Artificial Intelligence Lab [2] at the University of Arizona. ET-Map was developed in 1995 as part of innovative research in automatic Internet homepage categorization and it charts a large chunk of Yahoo!, from the entertainment section representing some 110,000 different Web links. The map is a two-dimensional, multi-layered category map; its aim is to provide an intuitive visual information browsing tool. ET-Map can be browsed interactively, explored and queried, using the familiar point-and-click navigation style of the Web to find information of interest.
The View From Above Browsing for a particular piece on information on the Web can often feel like being stuck in an unfamiliar part of town walking around at street level looking for a particular store. You know the store is around there somewhere, but your viewpoint at ground level is constrained. What you really want is to get above the streets, hovering half a mile or so up in the air, to see the whole neighbourhood. This kind of birds-eye view function has been memorably described by David D. Clark, Senior Research Scientist at MIT's Laboratory for Computer Science and the Chairman of the Invisible Worlds Protocol Advisory Board, as the missing "up button" on the browser [3] . ET-Map is a nice example of a prototype for Clark's "up-button" view of an information space. The goal of information maps, like ET-Map, is to provide the browser with a sense of the lie of the information landscape, what is where, the location of clusters and hotspots, what is related to what. Ideally, this 'big-picture' all-in-one visual summary needs to fit on a single standard computer screen. ET-Map is one of my favourite examples, but there are many other interesting information maps being developed by other researchers and companies (see inset at the bottom of this page). How does ET-Map work? Here is a sequence of screenshots of a typical browsing session with ET-Map, which ends with access to Web pages on jazz musician Miles Davis. You can also tryout ET-Map for yourself, using a fully working demo on the AI Lab's website [4] . We begin with the top-level map showing forty odd broad entertainment 'subject regions' represented by regularly shaped tiles. Each tile is a visual summary of a group of Web pages with similar content. These tiles are shaded different colours to differentiate them, while labels identify the subject of the tile and the number in brackets telling you how many individual Web page links it contains. ET-Map uses two important, but common-sense, spatial concepts in its organisation and representation of the Web. Firstly, the 'subject regions' size is directly related to the number of Web pages in that category. For example, the 'MUSIC' subject area contains over 11,000 pages and so has a much larger area than the neighbouring area of 'LIVE' which only has 4,300 odd pages. This is intuitively meaningful, as the largest tiles are visually more prominent on the map and are likely to be more significant as they contain the most links. In addition, a second spatial concept, that of neighbourhood proximity, is applied so 'subject regions' closely related in term of content are plotted close to each other on the map. For example, 'FILM' and 'YEAR'S OSCARS', at the bottom left, are neighbours in both semantic and spatial space. This make senses as many things in the real-world are ordered in this way, with things that are alike being spatially close together (e.g. layout of goods in a store, or books in a library). Importantly, ET-Map is also a multi-layer map, with sub-maps showing greater informational resolution through a finer degree of categorization. So for any subject region that contains more than two hundred Web pages, a second-level map, with more detailed categories is generated. This subdivision of information space is repeated down the hierarchy as far as necessary. In the example, the user selected the 'MUSIC' subject region which, not surprisingly, contained many thousands of pages. A second-level map with numerous different music categories is then presented to the user. Delving deeper, the user wants to learn more about jazz music, so clicking on the 'JAZZ' tile leads to a third-level map, a fine-grained map of jazz related Web pages. Finally, selecting the 'MILES DAVIS' subject region leads to more a conventional looking ranking of pages from which the user selects one to download.
ET-Map was created using a sophisticated AI technique called Kohonen self-organizing map, a neural network approach that has been used for automatic analysis and classification of semantic content of text documents like Web pages. I do not pretend to fully understand how this technique works; I tend to think of it as a clever 'black-box' that group together things that are alike [5] . It is a real challenge to automatically classify pages from a very heterogeneous information collection like the Web into categories that will match the conceptions of a typical user. Directories like Yahoo! tend to rely on the skill of human editors to achieve this. ET-Map is an interesting prototype that I think highlights well the potential for a map-based approach to Web browsing. I am surprised none of the major search engines or directories have introduced the option of mapping results. Although, I am sure many are working on ideas. People certainly need all the help they get, as Web growth shows no sign of slowing. Just last month it was reported that the Web had surpassed one billion indexable pages [6].
Information Maps There are many other fascinating examples that employ two dimensional interactive maps to provide a 'birds-eye' view of information. They use various underlying techniques of textual analysis and clustering to turn the mass of information into a useful summary map (see "Mining in Textual Mountains" in Mappa.Mundi Magazine). In terms of visual representations they can be divided into two groups, those that generate smooth surfaces and those that produce regular, tiled maps. Unfortunately, we don't have space to examine them in detail, but they are well worth spending some time exploring. I will be covering some of them in future columns.
Research Prototypes Visual SiteMap Developed by Xia Lin, based at the College of Library and Information Science, Drexel University. CVG Cyberspace geography visualization, developed by Luc Girardin, at The Graduate Institute of International Studies, Switzerland. WEBSOM Maps the thousands of articles posted on Usenet newsgroups. It is being developed by researchers at the Neural Networks Research Centre, Helsinki University of Technology in Finland. TreeMaps Developed by Brian Johnson, Ben Shneiderman and colleagues in the Human-Computer Interaction Lab at the University of Maryland. Commercial Information Maps: NewsMaps Provides interactive information landscapes summarizing daily news stories, developed Cartia, Inc. Web Squirrel Creates maps known as information farms. It is developed by Eastgate Systems, Inc. Umap Produces interactive maps of Web searches. Map of the Market An interactive map of the market performance of the stocks of major US corporations developed by SmartMoney.com."

0.0034331365 = product of:
  0.013732546 = sum of:
    0.013732546 = weight(_text_:information in 1700) [ClassicSimilarity], result of:
      0.013732546 = score(doc=1700,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.1551638 = fieldWeight in 1700, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=1700)
  0.25 = coord(1/4)

Abstract

The search giant is automatically building Knowledge Vault, a massive database that could give us unprecedented access to the world's facts GOOGLE is building the largest store of knowledge in human history - and it's doing so without any human help. Instead, Knowledge Vault autonomously gathers and merges information from across the web into a single base of facts about the world, and the people and objects in it.

0.0034153117 = product of:
  0.013661247 = sum of:
    0.013661247 = product of:
      0.027322493 = sum of:
        0.027322493 = weight(_text_:22 in 3447) [ClassicSimilarity], result of:
          0.027322493 = score(doc=3447,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.15476047 = fieldWeight in 3447, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=3447)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 2.2017 10:16:49

0.0030344925 = product of:
  0.01213797 = sum of:
    0.01213797 = weight(_text_:information in 947) [ClassicSimilarity], result of:
      0.01213797 = score(doc=947,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13714671 = fieldWeight in 947, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=947)
  0.25 = coord(1/4)

Abstract

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want

0.0030344925 = product of:
  0.01213797 = sum of:
    0.01213797 = weight(_text_:information in 1210) [ClassicSimilarity], result of:
      0.01213797 = score(doc=1210,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13714671 = fieldWeight in 1210, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1210)
  0.25 = coord(1/4)

Abstract

The current web consists of documents that are highly heterogeneous and hard for machines to understand. The Semantic Web is a progressive movement of the Word Wide Web, aiming at converting the current web of unstructured documents to the web of data. In the Semantic Web, web documents are annotated with metadata using standardized ontology language. These annotated documents are directly processable by machines and it highly improves their usability and usefulness. In Ericsson, similar problems occur. There are massive documents being created with well-defined structures. Though these documents are about domain specific knowledge and can have rich relations, they are currently managed by a traditional search engine, which ignores the rich domain specific information and presents few data to users. Motivated by the Semantic Web, we aim to find standard ways to process these documents, extract rich domain specific information and annotate these data to documents with formal markup languages. We propose this project to develop a domain specific search engine for processing different documents and building explicit relations for them. This research project consists of the three main focuses: examining different domain specific documents and finding ways to extract their metadata; integrating a text search engine with an ontology server; exploring novel ways to build relations for documents. We implement this system and demonstrate its functions. As a prototype, the system provides required features and will be extended in the future.

0.0030039945 = product of:
  0.012015978 = sum of:
    0.012015978 = weight(_text_:information in 866) [ClassicSimilarity], result of:
      0.012015978 = score(doc=866,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13576832 = fieldWeight in 866, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=866)
  0.25 = coord(1/4)

Abstract

ChatGPT, die künstliche Intelligenz der Stunde, ist von OpenAI entwickelt worden. Und OpenAI ist in der Vergangenheit nicht unerheblich von Microsoft unterstützt worden. Nun geht es ums Profitieren: Die KI soll in die Suchmaschine Bing eingebaut werden, was eine direkte Konkurrenz zu Googles Suchalgorithmen und Intelligenzen bedeutet. Bing war da bislang nicht sonderlich erfolgreich. Wie "The Information" mit Verweis auf zwei Insider berichtet, plant Microsoft, ChatGPT in seine Suchmaschine Bing einzubauen. Bereits im März könnte die neue, intelligente Suche verfügbar sein. Microsoft hatte zuvor auf der hauseigenen Messe Ignite zunächst die Integration des Bildgenerators DALL·E 2 in seine Suchmaschine angekündigt - ohne konkretes Startdatum jedoch. Fragt man ChatGPT selbst, bestätigt der Chatbot seine künftige Aufgabe noch nicht. Weiß aber um potentielle Vorteile.

0.0026279476 = product of:
  0.01051179 = sum of:
    0.01051179 = weight(_text_:information in 3245) [ClassicSimilarity], result of:
      0.01051179 = score(doc=3245,freq=12.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.11877254 = fieldWeight in 3245, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.01953125 = fieldNorm(doc=3245)
  0.25 = coord(1/4)

Content

Text des Artikels: "Wenn der Rohstoff des 21. Jahrhunderts wirklich Information ist, dann unterscheidet er sich grundlegend von seinen Vorgängern Eisenerz und Erdöl: Er verbraucht sich nicht, kann endlos wiederverwertet werden, ist einfach um die ganze Welt transportierbar, und kann sich sogar durch Neuzusammensetzung vermehren. Letztere Eigenschaft, so schön sie zunächst scheint, wird allmählich zur Plage. Die Menge der weltweit vorliegenden Information wächst seit Jahrhunderten stetig. Laut einem Bericht der University of California in Berkeley schafft die Menschheit derzeit ein bis zwei Exabyte (Milliarden Gigabyte) an Information pro Jahr. Dargestellt als Text entspricht das einem Stapel von rund einer Billion dicker Bücher - dessen Höhe etwa die 130-fachen Entfernung Erde-Mond erreichen würde. Die große Herausforderung ist, aus solch gigantischen Informationsmengen das subjektiv Wesentliche - also das Wissen - herauszusuchen. Die Wissensextraktion wird im digitalen Zeitalter zunehmend von Internet-Suchmaschinen übernommen. Sie verarbeiten den Rohstoff Information zu Wissen, kontrollieren und verteilen ihn. Es kann keinem Nutzer ganz geheuer sein, dass diese Schlüsselfunktion der Informationsgesellschaft in die Hände weniger Konzerne gerät: Google hat mit einem Marktanteil von mehr als 80 Prozent in Deutschland ein De-facto-Monopol erreicht, das nun Microsoft mit seiner "MSN Search" angreifen will. Aber diese Alternative weckt schwerlich mehr Vertrauen.
Suchmaschinen-Monopolisten können bestimmen oder kontrollieren, welche Information wann und auf welchen Rechnern verfügbar ist, und in welcher Reihenfolge die Ergebnisse angezeigt werden. Durch Beobachtung der Abrufe können die Unternehmen genaue Profile ihrer Nutzer erstellen. Um die Vormacht der kommerziellen Wissenswächter zu brechen, bedarf es einer freien Suchkultur - so wie das offene Betriebssystem Linux die Welt vor einer reinen Windows-Monokultur bewahrt hat. Immerhin scheint man auch auf staatlicher Seite das Problem des "Information Overkill" erkannt zu haben. Die öffentliche Hand fördert zahlreiche Projekte, die Ordnung in den Datenwust bringen wollen. Doch die meisten davon sind mehr visionär als realistisch. Vom einst so gefeierten "Semantic Web" etwa ist auch nach Jahren kaum Handfestes zu sehen. Kein Wunder: Solche Vorhaben setzen voraus, dass die Daten zunächst eingesammelt und suchgerecht indiziert werden. Mangels freier Software fehlt diese Voraussetzung. Was also ist nötig, um im Informationszeitalter die freie Verfügbarkeit der Ressourcen sicherzustellen? Die Antwort ist die gleiche wie einst für Kohle, Eisen und Öl: eine Vielfalt von Anbietern. Der beste Weg dorthin führt über freie Suchmaschinen-Software, auf welche die Betreiber solcher Maschinen zurückgreifen können. Dann entstünde ganz von selbst ein offener und dynamischer Wettbewerb. Freie Suchmaschinen-Software ist jedoch sehr rar. Es gibt Ansätze dazu in Russland und ein einziges Projekt in den USA (nutch.org). Auch Europa ist weitgehend Ödnis - bis auf den Lichtblick Yacy, ein Vorhaben des Frankfurter Softwarespezialisten Michael Christen. Yacy ist meines Wissen der weltweit einzige proof-of-concept einer strikt dezentralen Peer-to-Peer-Suchmaschine (suma-lab.de:8080"). Um die Suchmaschinen-Landschaft zu beleben, haben nun 13 Forscher, Politiker und Unternehmer den "Gemeinnützigen Verein zur Förderung der Suchmaschinen-Technologie und des freien Wissenszugangs" (kurz: SuMa-eV, suma-ev.de) mit Sitz in Hannover gegründet. Zu den Gründungsmitgliedern gehören der MP3-Erfinder Karlheinz Brandenburg, der Vizepräsident für Forschung der Universität Hannover Wolfgang Ertmer und ich selbst. Ziel des SuMa-eV ist die Etablierung einer auf möglichst viele autarke Systeme verteilten Suchmaschinen-Infrastruktur, die von ihrem Bauprinzip her kaum monopolisierbar ist. Der Kerngedanke dieser Struktur, die sich aus sehr vielen und sehr unterschiedlichen Bausteinen zusammensetzen kann, liegt in der Autarkie der Einzelsysteme: gesellschaftlicher Pluralismus wird netztopologisch abgebildet. Eigentlich wäre es im Interesse und in der Macht des Staats, die Meinungsvielfalt im Netz besser zu sichern. Während er - abgesehen von ein paar hellhörigen Parlamentariern - noch träumerische Visionen pflegt, müssen Initiativen wie SuMa-eV einspringen."

0.0026015358 = product of:
  0.010406143 = sum of:
    0.010406143 = weight(_text_:information in 3891) [ClassicSimilarity], result of:
      0.010406143 = score(doc=3891,freq=6.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.11757882 = fieldWeight in 3891, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3891)
  0.25 = coord(1/4)

Content

How should Google be regulated? We should limit the amount of information that is being collected. In particular we should look at information that is being collected across sites. It should not be legal to combine data from multiple sites and services. The fact that these sites and services are using the same underlying technology does not change the fact that the user's dealings is with a site at a time and each site should not have the right to share the data with others. I believe this the cornerstone of laws in many countries today, but these laws need to be enforced. Data about us is ours alone and it should not be possible to sell it. We should also limit the ability to target users individually. In the past, ads on sites were ads on sites. You might know what kind of users visited a site and you would place tech ads on tech sites and fashion ads on fashion sites. Now the ads follow you individually. That should be made illegal as it uses data collected from multiple sources and invades our privacy. I also believe there should be regulation as to how location data is used and any information related to our mobile devices. In addition, regulators need to be vigilant as to how companies that have monopoly power use their power. That kind of goes without saying. Companies with monopoly powers should not be able to use those powers when competing in an open market or using their monopoly services to limit competition."

0.0025748524 = product of:
  0.01029941 = sum of:
    0.01029941 = weight(_text_:information in 945) [ClassicSimilarity], result of:
      0.01029941 = score(doc=945,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.116372846 = fieldWeight in 945, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=945)
  0.25 = coord(1/4)

Abstract

The desalination industry involves the desalting of sea or brackish water and achieves the purpose of increasing the worls's effective water supply. There are approximately 4.000 desalination Web sites. The six major Internet search engines were used to determine, according to each of the six, the top twenty sites for desalination. Each site was visited and the 120 gross returns were pared down to the final ten - the 'Top Ten'. The Top Ten were then analyzed to determine what it was that made the sites useful and informative. The major attributes were: a) currency (up-to-date); b) search site capability; c) access to articles on desalination; d) newsletters; e) databases; f) product information; g) online conferencing; h) valuable links to other sites; l) communication links; j) site maps; and k) case studies. Reasons for having a Web site and the current status and prospects for Internet commerce are discussed

0.0025748524 = product of:
  0.01029941 = sum of:
    0.01029941 = weight(_text_:information in 5661) [ClassicSimilarity], result of:
      0.01029941 = score(doc=5661,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.116372846 = fieldWeight in 5661, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=5661)
  0.25 = coord(1/4)

Abstract

In a series of capsule reviews of 20 search engines Sirapyan gives a good overview of the state of Internet search tools. She starts out with a clear discussion of the types of search tools available, the availability of advanced features such as Boolean queries and differences between directories, regular search engines and metasearch engines. It is unclear from the article whether the author and other testers used the same searches across all of the 20 tools but each review clearly outlines perceived strengths and weaknesses, gives tips on the advanced features, if any, of the search tool in question and suggests the types of searches that are most successful. The tools which receive top honors are Google, Northern Light, HotBot and Oingo. Finally, there is an extra sidebar the discusses meta and specialized search tools such as Infozoid and FirstGov. I can't help thinking that the usefulness of this article is related to the fact that Sirapyan is PC Magazine's librarian and goes into greater depth on those features that are of interest to information professionals

0.0025748524 = product of:
  0.01029941 = sum of:
    0.01029941 = weight(_text_:information in 155) [ClassicSimilarity], result of:
      0.01029941 = score(doc=155,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.116372846 = fieldWeight in 155, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=155)
  0.25 = coord(1/4)

Abstract

Everyone loves images. The web wasn't anything until images came along, then it was an overnight success. So how does one find a specific image on the web? By using one of a burgeoning number of image-focused search engines. These search engines are simply optimized versions of typical web indexes, with crawlers that go around sucking down web content and indexing it. But with image search engines, they focus on images only, and the web page text that may describe them. As information professionals, we know that this is a clumsy approach at best, but as the author puts it, until more sophisticated methods become available, the tools profiled here will "have to suffice." Seven search engines are thoroughly tested in this review article, with Google's Image Search (http://www.google.com/imghp?hl=en) being the highest rated

0.002427594 = product of:
  0.009710376 = sum of:
    0.009710376 = weight(_text_:information in 2596) [ClassicSimilarity], result of:
      0.009710376 = score(doc=2596,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.10971737 = fieldWeight in 2596, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=2596)
  0.25 = coord(1/4)

Content

Ramana Rao (Inxight, Palo Alto, CA) 7 ± 2 Insights on achieving Effective Information Access Session One: Updates and a twelve month perspective Danny Sullivan (Search Engine Watch, US / England) Portalization and other search trends Carol Tenopir (University of Tennessee) Search realities faced by end users and professional searchers Session Two: Today's search engines and beyond Daniel Hoogterp (Retrieval Technologies, McLean, VA) Effective presentation and utilization of search techniques Rick Kenny (Fulcrum Technologies, Ontario, Canada) Beyond document clustering: The knowledge impact statement Gary Stock (Ingenius, Kalamazoo, MI) Automated change monitoring Gary Culliss (Direct Hit, Wellesley Hills, MA) User popularity ranked search engines Byron Dom (IBM, CA) Automatically finding the best pages on the World Wide Web (CLEVER) Peter Tomassi (LookSmart, San Francisco, CA) Adding human intellect to search technology Session Three: Panel discussion: Human v automated categorization and editing Ev Brenner (New York, NY)- Chairman James Callan (University of Massachusetts, MA) Marc Krellenstein (Northern Light Technology, Cambridge, MA) Dan Miller (Ask Jeeves, Berkeley, CA) Session Four: Updates and a twelve month perspective Steve Arnold (AIT, Harrods Creek, KY) Review: The leading edge in search and retrieval software Ellen Voorhees (NIST, Gaithersburg, MD) TREC update Session Five: Search engines now and beyond Intelligent Agents John Snyder (Muscat, Cambridge, England) Practical issues behind intelligent agents Text summarization Therese Firmin, (Dept of Defense, Ft George G. Meade, MD) The TIPSTER/SUMMAC evaluation of automatic text summarization systems Cross language searching Elizabeth Liddy (TextWise, Syracuse, NY) A conceptual interlingua approach to cross-language retrieval. Video search and retrieval Armon Amir (IBM, Almaden, CA) CueVideo: Modular system for automatic indexing and browsing of video/audio Speech recognition Michael Witbrock (Lycos, Waltham, MA) Retrieval of spoken documents Visualization James A. Wise (Integral Visuals, Richland, WA) Information visualization in the new millennium: Emerging science or passing fashion? Text mining David Evans (Claritech, Pittsburgh, PA) Text mining - towards decision support

0.002427594 = product of:
  0.009710376 = sum of:
    0.009710376 = weight(_text_:information in 1196) [ClassicSimilarity], result of:
      0.009710376 = score(doc=1196,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.10971737 = fieldWeight in 1196, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=1196)
  0.25 = coord(1/4)

Abstract

This article describes the journey from the conception of and vision for a modern search-engine-based search environment to its technological realisation. In doing so, it takes up the thread of an earlier article on this subject, this time from a technical viewpoint. As well as presenting the conceptual considerations of the initial stages, this article will principally elucidate the technological aspects of this journey. The starting point for the deliberations about development of an academic search engine was the experience we gained through the generally successful project "Digital Library NRW", in which from 1998 to 2000-with Bielefeld University Library in overall charge-we designed a system model for an Internet-based library portal with an improved academic search environment at its core. At the heart of this system was a metasearch with an availability function, to which we added a user interface integrating all relevant source material for study and research. The deficiencies of this approach were felt soon after the system was launched in June 2001. There were problems with the stability and performance of the database retrieval system, with the integration of full-text documents and Internet pages, and with acceptance by users, because users are increasingly performing the searches themselves using search engines rather than going to the library for help in doing searches. Since a long list of problems are also encountered using commercial search engines for academic use (in particular the retrieval of academic information and long-term availability), the idea was born for a search engine configured specifically for academic use. We also hoped that with one single access point founded on improved search engine technology, we could access the heterogeneous academic resources of subject-based bibliographic databases, catalogues, electronic newspapers, document servers and academic web pages.

Theme

Information Gateway

0.0022298878 = product of:
  0.008919551 = sum of:
    0.008919551 = weight(_text_:information in 3071) [ClassicSimilarity], result of:
      0.008919551 = score(doc=3071,freq=6.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.10078184 = fieldWeight in 3071, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3071)
  0.25 = coord(1/4)

Content

"One of the things I enjoyed the most when I was a reference librarian was the wide range of questions my clients sent my way. What was the original title of the first Godzilla movie? (Gojira, released in 1954) Who said 'I'm as pure as the driven slush'? (Tallulah Bankhead) What percentage of adults have gone to a jazz performance in the last year? (11%) I have found that librarians, speech writers and journalists have one thing in common - we all need to find information on all kinds of topics, and we usually need the answers right now. The following are a few of my favorite sites for finding answers to those there-must-be-an-answer-out-there questions. - For the electronic equivalent to the "ready reference" shelf of resources that most librarians keep hidden behind their desks, check out RefDesk . It is particularly good for answering factual questions - Where do I get the new Windows XP Service Pack? Where is the 386 area code? How do I contact my member of Congress? - Another resource for lots of those quick-fact questions is InfoPlease, the publishers of the Information Please almanac .- Right now, it's full of Olympics data, but it also has links to facts and factoids that you would look up in an almanac, atlas, or encyclopedia. - If you want numbers, start with the Statistical Abstract of the US. This source, produced by the U.S. Census Bureau, gives you everything from the divorce rate by state to airline cost indexes going back to 1980. It is many librarians' secret weapon for pulling numbers together quickly. - My favorite question is "how does that work?" Haven't you ever wondered how they get that Olympic torch to continue to burn while it is being carried by runners from one city to the next? Or how solar sails manage to propel a spacecraft? For answers, check out the appropriately-named How Stuff Works. - For questions about movies, my first resource is the Internet Movie Database. It is easy to search, is such a popular site that mistakes are corrected quickly, and is a fun place to catch trailers of both upcoming movies and those dating back to the 30s. - When I need to figure out who said what, I still tend to rely on the print sources such as Bartlett's Familiar Quotations . No, the current edition is not available on the web, but - and this is the librarian in me - I really appreciate the fact that I not only get the attribution but I also see the source of the quote. There are far too many quotes being attributed to a celebrity, but with no indication of the publication in which the quote appeared. Take, for example, the much-cited quote of Margaret Meade, "Never doubt that a small group of thoughtful committed people can change the world; indeed, it's the only thing that ever has!" Then see the page on the Institute for Intercultural Studies site, founded by Meade, and read its statement that it has never been able to verify this alleged quote from Meade. While there are lots of web-based sources of quotes (see QuotationsPage.com and Bartleby, for example), unless the site provides the original source for the quotation, I wouldn't rely on the citation. Of course, if you have a hunch as to the source of a quote, and it was published prior to 1923, head over to Project Gutenberg , which includes the full text of over 12,000 books that are in the public domain. When I needed to confirm a quotation of the Red Queen in "Through the Looking Glass", this is where I started. - And if you are stumped as to where to go to find information, instead of Googling it, try the Librarians' Index to the Internet. While it is somewhat US-centric, it is a great directory of web resources."

0.0021457102 = product of:
  0.008582841 = sum of:
    0.008582841 = weight(_text_:information in 3068) [ClassicSimilarity], result of:
      0.008582841 = score(doc=3068,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 3068, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3068)
  0.25 = coord(1/4)

Source

Young information scientists. 1(2016), S.13-29

0.0021457102 = product of:
  0.008582841 = sum of:
    0.008582841 = weight(_text_:information in 3144) [ClassicSimilarity], result of:
      0.008582841 = score(doc=3144,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 3144, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3144)
  0.25 = coord(1/4)

Abstract

Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.

0.0017165683 = product of:
  0.006866273 = sum of:
    0.006866273 = weight(_text_:information in 4709) [ClassicSimilarity], result of:
      0.006866273 = score(doc=4709,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.0775819 = fieldWeight in 4709, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=4709)
  0.25 = coord(1/4)

Content

"Swoogle, the Semantic web search engine, is a research project carried out by the ebiquity research group in the Computer Science and Electrical Engineering Department at the University of Maryland. It's an engine tailored towards finding documents on the semantic web. The whole research paper is available here. Semantic web is touted as the next generation of online content representation where the web documents are represented in a language that is not only easy for humans but is machine readable (easing the integration of data as never thought possible) as well. And the main elements of the semantic web include data model description formats such as Resource Description Framework (RDF), a variety of data interchange formats (e.g. RDF/XML, Turtle, N-Triples), and notations such as RDF Schema (RDFS), the Web Ontology Language (OWL), all of which are intended to provide a formal description of concepts, terms, and relationships within a given knowledge domain (Wikipedia). And Swoogle is an attempt to mine and index this new set of web documents. The engine performs crawling of semantic documents like most web search engines and the search is available as web service too. The engine is primarily written in Java with the PHP used for the front-end and MySQL for database. Swoogle is capable of searching over 10,000 ontologies and indexes more that 1.3 million web documents. It also computes the importance of a Semantic Web document. The techniques used for indexing are the more google-type page ranking and also mining the documents for inter-relationships that are the basis for the semantic web. For more information on how the RDF framework can be used to relate documents, read the link here. Being a research project, and with a non-commercial motive, there is not much hype around Swoogle. However, the approach to indexing of Semantic web documents is an approach that most engines will have to take at some point of time. When the Internet debuted, there were no specific engines available for indexing or searching. The Search domain only picked up as more and more content became available. One fundamental question that I've always wondered about it is - provided that the search engines return very relevant results for a query - how to ascertain that the documents are indeed the most relevant ones available. There is always an inherent delay in indexing of document. Its here that the new semantic documents search engines can close delay. Experimenting with the concept of Search in the semantic web can only bore well for the future of search technology."