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Radhakrishnan, A.: Swoogle : an engine for the Semantic Web (2007)
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- 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."
-
Perez, E.: dtSearch: the little search engine that could (2004)
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- Online. 29(2005) no.1, S.28-
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Langville, A.N.; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings (2006)
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- Content
- Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling
-
MacLeod, R.: Promoting a subject gateway : a case study from EEVL (Edinburgh Engineering Virtual Library) (2000)
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- Date
- 22. 6.2002 19:40:22
-
Courtois, M.P.: Cool tools for searching the Web : an update (1996)
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- Source
- Online. 20(1996) no.3, S.29-36
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Sherman, C.: ¬The future of Web search (1999)
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- Date
- 1. 6.2000 14:29:46
-
Feldman, S.: New study of Web search engine coverage published (1999)
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- Source
- Information today. 16(1999) no.8, S.29
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Tan, W.: Subject access on Internet : highlights of the metasearch engines (1998)
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- Source
- Journal of educational media and library sciences. 36(1998) no.1, S.20-29
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Vidmar, D.J.: Darwin on the Web : the evolution of search tools (1999)
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- Computers in libraries. 19(1999) no.5, S.22-28
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Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
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- Date
- 25. 8.2005 17:42:22
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Dunning, A.: Do we still need search engines? (1999)
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- Ariadne. 1999, no.22
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Bawden, D.: Google and the universe of knowledge (2008)
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- Date
- 7. 6.2008 16:22:20
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Sherman, C.: Reference resources on the Web (2000)
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- Date
- 29. 3.2002 17:49:46
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Birmingham, J.: Internet search engines (1996)
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- 10.11.1996 16:36:22
-
Hock, R.E.: How to do field searching in Web search engines : a field trip (1998)
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Dodge, M.: ¬A map of Yahoo! (2000)
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- Content
- 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.
-
Koch, T.: Searching the Web : systematic overview over indexes (1995)
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- Pages
- S.29-61
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Fryxell, D.A.: ¬9 Web search sites examined (1996)
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- Link-up. 13(1996) no.2, S.29-30
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Courlois, M.P.: Cool tools for Web searching : an update (1996)
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Price, A.: Five new Danish subject gateways under development (2000)
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- Date
- 22. 6.2002 19:41:31
