Search (70 results, page 1 of 4)

  • × year_i:[2000 TO 2010}
  • × theme_ss:"Retrievalalgorithmen"
  1. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.14 
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    Abstract
    Im Rahmen des klassischen Information Retrieval wurden verschiedene Verfahren für das Ranking sowie die Suche in einer homogenen strukturlosen Dokumentenmenge entwickelt. Die Erfolge der Suchmaschine Google haben gezeigt dass die Suche in einer zwar inhomogenen aber zusammenhängenden Dokumentenmenge wie dem Internet unter Berücksichtigung der Dokumentenverbindungen (Links) sehr effektiv sein kann. Unter den von der Suchmaschine Google realisierten Konzepten ist ein Verfahren zum Ranking von Suchergebnissen (PageRank), das in diesem Artikel kurz erklärt wird. Darüber hinaus wird auf die Konzepte eines Systems namens CiteSeer eingegangen, welches automatisch bibliographische Angaben indexiert (engl. Autonomous Citation Indexing, ACI). Letzteres erzeugt aus einer Menge von nicht vernetzten wissenschaftlichen Dokumenten eine zusammenhängende Dokumentenmenge und ermöglicht den Einsatz von Banking-Verfahren, die auf den von Google genutzten Verfahren basieren.
    Date
    20. 3.2005 16:23:22
  2. Kaszkiel, M.; Zobel, J.: Effective ranking with arbitrary passages (2001) 0.07 
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    Abstract
    Text retrieval systems store a great variety of documents, from abstracts, newspaper articles, and Web pages to journal articles, books, court transcripts, and legislation. Collections of diverse types of documents expose shortcomings in current approaches to ranking. Use of short fragments of documents, called passages, instead of whole documents can overcome these shortcomings: passage ranking provides convenient units of text to return to the user, can avoid the difficulties of comparing documents of different length, and enables identification of short blocks of relevant material among otherwise irrelevant text. In this article, we compare several kinds of passage in an extensive series of experiments. We introduce a new type of passage, overlapping fragments of either fixed or variable length. We show that ranking with these arbitrary passages gives substantial improvements in retrieval effectiveness over traditional document ranking schemes, particularly for queries on collections of long documents. Ranking with arbitrary passages shows consistent improvements compared to ranking with whole documents, and to ranking with previous passage types that depend on document structure or topic shifts in documents
    Date
    29. 9.2001 14:00:39
  3. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.06 
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    Date
    25. 8.2005 17:42:22
  4. Vechtomova, O.; Karamuftuoglu, M.: Lexical cohesion and term proximity in document ranking (2008) 0.06 
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    Abstract
    We demonstrate effective new methods of document ranking based on lexical cohesive relationships between query terms. The proposed methods rely solely on the lexical relationships between original query terms, and do not involve query expansion or relevance feedback. Two types of lexical cohesive relationship information between query terms are used in document ranking: short-distance collocation relationship between query terms, and long-distance relationship, determined by the collocation of query terms with other words. The methods are evaluated on TREC corpora, and show improvements over baseline systems.
    Date
    1. 8.2008 12:29:05
  5. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (2005) 0.06 
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    Content
    Inhalt: Introduction Document File Preparation - Manual Indexing - Information Extraction - Vector Space Modeling - Matrix Decompositions - Query Representations - Ranking and Relevance Feedback - Searching by Link Structure - User Interface - Book Format Document File Preparation Document Purification and Analysis - Text Formatting - Validation - Manual Indexing - Automatic Indexing - Item Normalization - Inverted File Structures - Document File - Dictionary List - Inversion List - Other File Structures Vector Space Models Construction - Term-by-Document Matrices - Simple Query Matching - Design Issues - Term Weighting - Sparse Matrix Storage - Low-Rank Approximations Matrix Decompositions QR Factorization - Singular Value Decomposition - Low-Rank Approximations - Query Matching - Software - Semidiscrete Decomposition - Updating Techniques Query Management Query Binding - Types of Queries - Boolean Queries - Natural Language Queries - Thesaurus Queries - Fuzzy Queries - Term Searches - Probabilistic Queries Ranking and Relevance Feedback Performance Evaluation - Precision - Recall - Average Precision - Genetic Algorithms - Relevance Feedback Searching by Link Structure HITS Method - HITS Implementation - HITS Summary - PageRank Method - PageRank Adjustments - PageRank Implementation - PageRank Summary User Interface Considerations General Guidelines - Search Engine Interfaces - Form Fill-in - Display Considerations - Progress Indication - No Penalties for Error - Results - Test and Retest - Final Considerations Further Reading
    RSWK
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
    Subject
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
  6. Stock, M.; Stock, W.G.: Internet-Suchwerkzeuge im Vergleich (IV) : Relevance Ranking nach "Popularität" von Webseiten: Google (2001) 0.05 
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    Abstract
    In unserem Retrievaltest von Suchwerkzeugen im World Wide Web (Password 11/2000) schnitt die Suchmaschine Google am besten ab. Im Vergleich zu anderen Search Engines setzt Google kaum auf Informationslinguistik, sondern auf Algorithmen, die sich aus den Besonderheiten der Web-Dokumente ableiten lassen. Kernstück der informationsstatistischen Technik ist das "PageRank"- Verfahren (benannt nach dem Entwickler Larry Page), das aus der Hypertextstruktur des Web die "Popularität" von Seiten anhand ihrer ein- und ausgehenden Links berechnet. Google besticht durch das Angebot intuitiv verstehbarer Suchbildschirme sowie durch einige sehr nützliche "Kleinigkeiten" wie die Angabe des Rangs einer Seite, Highlighting, Suchen in der Seite, Suchen innerhalb eines Suchergebnisses usw., alles verstaut in einer eigenen Befehlsleiste innerhalb des Browsers. Ähnlich wie RealNames bietet Google mit dem Produkt "AdWords" den Aufkauf von Suchtermen an. Nach einer Reihe von nunmehr vier Password-Artikeln über InternetSuchwerkzeugen im Vergleich wollen wir abschließend zu einer Bewertung kommen. Wie ist der Stand der Technik bei Directories und Search Engines aus informationswissenschaftlicher Sicht einzuschätzen? Werden die "typischen" Internetnutzer, die ja in der Regel keine Information Professionals sind, adäquat bedient? Und können auch Informationsfachleute von den Suchwerkzeugen profitieren?
  7. Langville, A.N.; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings (2006) 0.05 
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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
    Chapter 9. Accelerating the Computation of PageRank: 9.1 An Adaptive Power Method - 9.2 Extrapolation - 9.3 Aggregation - 9.4 Other Numerical Methods Chapter 10. Updating the PageRank Vector: 10.1 The Two Updating Problems and their History - 10.2 Restarting the Power Method - 10.3 Approximate Updating Using Approximate Aggregation - 10.4 Exact Aggregation - 10.5 Exact vs. Approximate Aggregation - 10.6 Updating with Iterative Aggregation - 10.7 Determining the Partition - 10.8 Conclusions Chapter 11. The HITS Method for Ranking Webpages: 11.1 The HITS Algorithm - 11.2 HITS Implementation - 11.3 HITS Convergence - 11.4 HITS Example - 11.5 Strengths and Weaknesses of HITS - 11.6 HITS's Relationship to Bibliometrics - 11.7 Query-Independent HITS - 11.8 Accelerating HITS - 11.9 HITS Sensitivity Chapter 12. Other Link Methods for Ranking Webpages: 12.1 SALSA - 12.2 Hybrid Ranking Methods - 12.3 Rankings based on Traffic Flow Chapter 13. The Future of Web Information Retrieval: 13.1 Spam - 13.2 Personalization - 13.3 Clustering - 13.4 Intelligent Agents - 13.5 Trends and Time-Sensitive Search - 13.6 Privacy and Censorship - 13.7 Library Classification Schemes - 13.8 Data Fusion Chapter 14. Resources for Web Information Retrieval: 14.1 Resources for Getting Started - 14.2 Resources for Serious Study Chapter 15. The Mathematics Guide: 15.1 Linear Algebra - 15.2 Perron-Frobenius Theory - 15.3 Markov Chains - 15.4 Perron Complementation - 15.5 Stochastic Complementation - 15.6 Censoring - 15.7 Aggregation - 15.8 Disaggregation
    RSWK
    Google / Suchmaschine / Ranking (BVB)
    Subject
    Google / Suchmaschine / Ranking (BVB)
  8. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.05 
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    Abstract
    Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR taskdiscovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is weIl known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs an GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations an the design of fitness functions for genetic-based information retrieval experiments.
    Date
    31. 5.2004 19:22:06
  9. Lewandowski, D.: How can library materials be ranked in the OPAC? (2009) 0.03 
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    Abstract
    Some Online Public Access Catalogues offer a ranking component. However, ranking there is merely text-based and is doomed to fail due to limited text in bibliographic data. The main assumption for the talk is that we are in a situation where the appropriate ranking factors for OPACs should be defined, while the implementation is no major problem. We must define what we want, and not so much focus on the technical work. Some deep thinking is necessary on the "perfect results set" and how we can achieve it through ranking. The talk presents a set of potential ranking factors and clustering possibilities for further discussion. A look at commercial Web search engines could provide us with ideas how ranking can be improved with additional factors. Search engines are way beyond pure text-based ranking and apply ranking factors in the groups like popularity, freshness, personalisation, etc. The talk describes the main factors used in search engines and how derivatives of these could be used for libraries' purposes. The goal of ranking is to provide the user with the best-suitable results on top of the results list. How can this goal be achieved with the library catalogue and also concerning the library's different collections and databases? The assumption is that ranking of such materials is a complex problem and is yet nowhere near solved. Libraries should focus on ranking to improve user experience.
  10. Wechsler, M.; Schäuble, P.: ¬The probability ranking principle revisited (2000) 0.03 
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  11. Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003) 0.03 
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    Abstract
    Bekanntlich führen Suchmaschinen wie Google &Co. beider Auflistung der Suchergebnisse ein "Ranking" nach "Relevanz" durch, d.h. die Dokumente werden in absteigender Reihenfolge entsprechend ihrer Erfüllung von Relevanzkriterien ausgeben. In Online-Katalogen (OPACs) ist derlei noch nicht allgemein übliche Praxis, doch bietet etwa das im Österreichischen Bibliothekenverbund eingesetzte System Aleph 500 tatsächlich eine solche Ranking-Option an (die im Verbundkatalog auch implementiert ist). Bislang liegen allerdings kaum Informationen zur Funktionsweise dieses Features, insbesondere auch im Hinblick auf eine Hilfestellung für Benutzer, vor. Daher möchten wir mit diesem Beitrag versuchen, den in unserem Verbund bestehenden Informationsstand zum Thema "Relevance Ranking" zu erweitern. Sowohl die Verwendung einer Ranking-Option in OPACs generell als auch die sich unter Aleph 500 konkret bietenden Möglichkeiten sollen im folgenden näher betrachtet werden.
  12. Bidoki, A.M.Z.; Yazdani, N.: an intelligent ranking algorithm for web pages : DistanceRank (2008) 0.03 
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    Abstract
    A fast and efficient page ranking mechanism for web crawling and retrieval remains as a challenging issue. Recently, several link based ranking algorithms like PageRank, HITS and OPIC have been proposed. In this paper, we propose a novel recursive method based on reinforcement learning which considers distance between pages as punishment, called "DistanceRank" to compute ranks of web pages. The distance is defined as the number of "average clicks" between two pages. The objective is to minimize punishment or distance so that a page with less distance to have a higher rank. Experimental results indicate that DistanceRank outperforms other ranking algorithms in page ranking and crawling scheduling. Furthermore, the complexity of DistanceRank is low. We have used University of California at Berkeley's web for our experiments.
  13. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.03 
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    Abstract
    A relevancy-ranking algorithm for a natural language interface to Boolean online public access catalogs (OPACs) was formulated and compared with that currently used in a knowledge-based search interface called the E-Referencer, being developed by the authors. The algorithm makes use of seven weIl-known ranking criteria: breadth of match, section weighting, proximity of query words, variant word forms (stemming), document frequency, term frequency and document length. The algorithm converts a natural language query into a series of increasingly broader Boolean search statements. In a small experiment with ten subjects in which the algorithm was simulated by hand, the algorithm obtained good results with a mean overall precision of 0.42 and mean average precision of 0.62, representing a 27 percent improvement in precision and 41 percent improvement in average precision compared to the E-Referencer. The usefulness of each step in the algorithm was analyzed and suggestions are made for improving the algorithm.
    Content
    "Most Web search engines accept natural language queries, perform some kind of fuzzy matching and produce ranked output, displaying first the documents that are most likely to be relevant. On the other hand, most library online public access catalogs (OPACs) an the Web are still Boolean retrieval systems that perform exact matching, and require users to express their search requests precisely in a Boolean search language and to refine their search statements to improve the search results. It is well-documented that users have difficulty searching Boolean OPACs effectively (e.g. Borgman, 1996; Ensor, 1992; Wallace, 1993). One approach to making OPACs easier to use is to develop a natural language search interface that acts as a middleware between the user's Web browser and the OPAC system. The search interface can accept a natural language query from the user and reformulate it as a series of Boolean search statements that are then submitted to the OPAC. The records retrieved by the OPAC are ranked by the search interface before forwarding them to the user's Web browser. The user, then, does not need to interact directly with the Boolean OPAC but with the natural language search interface or search intermediary. The search interface interacts with the OPAC system an the user's behalf. The advantage of this approach is that no modification to the OPAC or library system is required. Furthermore, the search interface can access multiple OPACs, acting as a meta search engine, and integrate search results from various OPACs before sending them to the user. The search interface needs to incorporate a method for converting the user's natural language query into a series of Boolean search statements, and for ranking the OPAC records retrieved. The purpose of this study was to develop a relevancyranking algorithm for a search interface to Boolean OPAC systems. This is part of an on-going effort to develop a knowledge-based search interface to OPACs called the E-Referencer (Khoo et al., 1998, 1999; Poo et al., 2000). E-Referencer v. 2 that has been implemented applies a repertoire of initial search strategies and reformulation strategies to retrieve records from OPACs using the Z39.50 protocol, and also assists users in mapping query keywords to the Library of Congress subject headings."
    Source
    Electronic library. 22(2004) no.2, S.112-120
  14. Meghabghab, G.: Google's Web page ranking applied to different topological Web graph structures (2001) 0.03 
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    Abstract
    This research is part of the ongoing study to better understand web page ranking on the web. It looks at a web page as a graph structure or a web graph, and tries to classify different web graphs in the new coordinate space: (out-degree, in-degree). The out-degree coordinate od is defined as the number of outgoing web pages from a given web page. The in-degree id coordinate is the number of web pages that point to a given web page. In this new coordinate space a metric is built to classify how close or far different web graphs are. Google's web ranking algorithm (Brin & Page, 1998) on ranking web pages is applied in this new coordinate space. The results of the algorithm has been modified to fit different topological web graph structures. Also the algorithm was not successful in the case of general web graphs and new ranking web algorithms have to be considered. This study does not look at enhancing web ranking by adding any contextual information. It only considers web links as a source to web page ranking. The author believes that understanding the underlying web page as a graph will help design better ranking web algorithms, enhance retrieval and web performance, and recommends using graphs as a part of visual aid for browsing engine designers
  15. Weinstein, A.: Hochprozentig : Tipps and tricks für ein Top-Ranking (2002) 0.03 
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    Abstract
    Die Suchmaschinen haben in den letzten Monaten an ihren Ranking-Algorithmen gefeilt, um Spamern das Handwerk zu erschweren. Internet Pro beleuchtet die Trends im Suchmaschinen-Marketing
  16. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.03 
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  17. Clarke, C.L.A.; Cormack, G.V.; Tudhope, E.A.: Relevance ranking for one to three term queries (2000) 0.03 
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  18. Käki, M.: fKWIC: frequency-based Keyword-in-Context Index for filtering Web search results (2006) 0.03 
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    Abstract
    Enormous Web search engine databases combined with short search queries result in large result sets that are often difficult to access. Result ranking works fairly well, but users need help when it fails. For these situations, we propose a filtering interface that is inspired by keyword-in-context (KWIC) indices. The user interface lists the most frequent keyword contexts (fKWIC). When a context is selected, the corresponding results are displayed in the result list, allowing users to concentrate on the specific context. We compared the keyword context index user interface to the rank order result listing in an experiment with 36 participants. The results show that the proposed user interface was 29% faster in finding relevant results, and the precision of the selected results was 19% higher. In addition, participants showed positive attitudes toward the system.
  19. Kekäläinen, J.: Binary and graded relevance in IR evaluations : comparison of the effects on ranking of IR systems (2005) 0.03 
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    Date
    26.12.2007 20:29:18
  20. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.03 
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    Abstract
    Previous work an search-engine design has indicated that information-seekers may benefit from being given the opportunity to exploit multiple sources of evidence of document relatedness. Few existing systems, however, give users more than minimal control over the selections that may be made among methods of exploitation. By applying the methods of "document network analysis" (DNA), a unifying, graph-theoretic model of content-, collaboration-, and context-based systems (CCC) may be developed in which the nature of the similarities between types of document relatedness and document ranking are clarified. The usefulness of the approach to system design suggested by this model may be tested by constructing and evaluating a prototype system (UCXtra) that allows searchers to maintain control over the multiple ways in which document collections may be ranked and re-ranked.
    Date
    11. 9.2004 17:32:22

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