Literatur zur Informationserschließung
Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft
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1Bhansali, D. ; Desai, H. ; Deulkar, K.: ¬A study of different ranking approaches for semantic search.
In: International journal of computer applications. 129(2015) no.5, S1215.
Abstract: Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.
Inhalt: Vgl. auch: http://www.ijcaonline.org/research/volume129/number5/bhansali2015ijca906896.pdf.
Themenfeld: Suchmaschinen ; Retrievalalgorithmen ; Semantisches Umfeld in Indexierung u. Retrieval
Objekt: SemRank ; HITS

2Cheng, S. ; YunTao, P. ; JunPeng, Y. ; Hong, G. ; ZhengLu, Y. ; ZhiYu, H.: PageRank, HITS and impact factor for journal ranking.
In: Proceeding CSIE '09: Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering  Volume 06. Washington, DC : IEEE Computer Society, 2009. S.285290.
Abstract: Journal citation measures are one of the most widely used bibliometric tools. The most wellknown measure is the ISI Impact Factor, under the standard definition, the impact factor of journal j in a given year is the average number of citations received by papers published in the previous two years of journal j. However, the impact factor has its "intrinsic" limitations, it is a ranking measure based fundamentally on a pure counting of the indegrees of nodes in the network, and its calculation does not take into account the "impact" or "prestige" of the journals in which the citations appear. Google's PageRank algorithm and Kleinberg's HITS method are webpage ranking algorithm, they compute the scores of webpages based on a combination of the number of hyperlinks that point to the page and the status of pages that the hyperlinks originate from, a page is important if it is pointed to by other important pages. We demonstrate how popular webpage algorithm PageRank and HITS can be used ranking journal, and we compared ISI impact factor, PageRank and HITS for journal ranking, and with PageRank and HITS compute respectively including selfcitation and non selfcitation, and discussed the merit and shortcomings and the scope of application that the various algorithms are used to rank journal.
Inhalt: Vgl.: DOI: 10.1109/CSIE.2009.351.
Themenfeld: Suchmaschinen ; Informetrie
Objekt: PageRank ; HITS

3Langville, A.N. ; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings.
Princeton : Princeton Univ. Press, 2006. X, 224 S.
ISBN 0691122024
Abstract: Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, "Google's PageRank and Beyond" supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample Web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. It includes: many illustrative examples and entertaining asides; MATLAB code; accessible and informal style; and complete and selfcontained section for mathematics review.
Inhalt: 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 QueryIndependence 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 LargeScale 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 QueryIndependent 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 TimeSensitive 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 PerronFrobenius Theory  15.3 Markov Chains  15.4 Perron Complementation  15.5 Stochastic Complementation  15.6 Censoring  15.7 Aggregation  15.8 Disaggregation
Themenfeld: Suchmaschinen ; Retrievalalgorithmen
Objekt: Google ; PageRank ; HITSAlgorithmus
RSWK: Google / WebSeite / Rangstatistik (HEBIS) ; Webpage / Rangstatistik (GBV) ; Google / Suchmaschine / Ranking (BVB)
BK: 54.65 / Webentwicklung / Webanwendungen ; 31.80 / Angewandte Mathematik ; 54.32 / Rechnerkommunikation ; 06.74 / Informationssysteme ; 06.70 / Katalogisierung / Bestandserschließung

4Agosti, M. ; Pretto, L.: ¬A theoretical study of a generalized version of kleinberg's HITS algorithm.
In: Advances in mathematical/formal methods in information retrieval. 8(2005) no.2 , S.219243.
Abstract: Kleinberg's HITS (HyperlinkInduced Topic Search) algorithm (Kleinberg 1999), which was originally developed in a Web context, tries to infer the authoritativeness of a Web page in relation to a specific query using the structure of a subgraph of the Web graph, which is obtained considering this specific query. Recent applications of this algorithm in contexts far removed from that of Web searching (Bacchin, Ferro and Melucci 2002, Ng et al. 2001) inspired us to study the algorithm in the abstract, independently of its particular applications, trying to mathematically illuminate its behaviour. In the present paper we detail this theoretical analysis. The original work starts from the definition of a revised and more general version of the algorithm, which includes the classic one as a particular case. We perform an analysis of the structure of two particular matrices, essential to studying the behaviour of the algorithm, and we prove the convergence of the algorithm in the most general case, finding the analytic expression of the vectors to which it converges. Then we study the symmetry of the algorithm and prove the equivalence between the existence of symmetry and the independence from the order of execution of some basic operations on initial vectors. Finally, we expound some interesting consequences of our theoretical results.
Themenfeld: Suchmaschinen ; Retrievalalgorithmen
Objekt: HITSAlgorithmus

5Berry, M.W. ; Browne, M.: Understanding search engines : mathematical modeling and text retrieval.2nd ed.
Philadelphia, PA : SIAM, 2005. XVII, 117 S.
ISBN 0898715814
(Software, environments, tools; 17)
Abstract: The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as usercentered issues such as interfaces, manual indexing, and document preparation. Significant changes bring the text up to date on current information retrieval methods: for example the addition of a new chapter on linkstructure algorithms used in search engines such as Google. The chapter on user interface has been rewritten to specifically focus on search engine usability. In addition the authors have added new recommendations for further reading and expanded the bibliography, and have updated and streamlined the index to make it more reader friendly.
Inhalt: 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  TermbyDocument Matrices  Simple Query Matching  Design Issues  Term Weighting  Sparse Matrix Storage  LowRank Approximations Matrix Decompositions QR Factorization  Singular Value Decomposition  LowRank 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 Fillin  Display Considerations  Progress Indication  No Penalties for Error  Results  Test and Retest  Final Considerations Further Reading
Themenfeld: Suchmaschinen ; Retrievalalgorithmen
Objekt: HITSAlgorithmus ; PageRank
LCSH: Web search engines ; Vector spaces ; Text processing (Computer science)
RSWK: Suchmaschine / Information Retrieval ; Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
BK: 06.44 / IuDEinrichtungen ; 06.74 / Informationssysteme ; 31.80 / Angewandte Mathematik
DDC: 025.04
LCC: TK5105.884.B47 2005

6Chakrabarti, S. ; Dom, B. ; Kumar, S.R. ; Raghavan, P. ; Rajagopalan, S. ; Tomkins, A. ; Kleinberg, J.M. ; Gibson, D.: Neue Pfade durch den InternetDschungel : Die zweite Generation von WebSuchmaschinen.
In: Spektrum der Wissenschaft. 1999, H.8, S.4449.
Abstract: Die im WWW verfügbare Datenmenge wächst mit atemberaubender Geschwindigkeit; entsprechend schwieriger wird es, relevante Informationen zu finden. ein neues Analyseverfahren stellt nahezu automatische Abhilfe in Aussicht
Inhalt: Ausnutzen der Hyperlinks für verbesserte Such und Findeverfahren; Darstellung des HITSAlgorithmus
Anmerkung: Vgl. auch: http://www.almaden.ibm.com/cs/k53/clever.html
Themenfeld: Suchmaschinen ; Retrievalalgorithmen
Objekt: Google ; HITSAlgorithmus

7Kleinberg, J.M.: Authoritative sources in a hyperlinked environment.
In: Journal of the Association for Computing Machinery. 46(1998) no.5, S.604632.
Abstract: The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of "authoritative" information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of "hub pages" that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for linkbased analysis.
Inhalt: Vorversionen auch in: Proceedings of the ACMSIAM Symposium on Discrete Algorithms, 1998, und als IBM Research Report RJ 10076, May 1997.
Themenfeld: Retrievalalgorithmen
Objekt: HITSAlgorithmus

8Li, L. ; Shang, Y. ; Zhang, W.: Improvement of HITSbased algorithms on Web documents.
In: WWW '02: Proceedings of the 11th International Conference on World Wide Web, May 711, 2002, Honolulu, Hawaii, USA. New York : ACM, S.527535.
Abstract: In this paper, we present two ways to improve the precision of HITSbased algorithms onWeb documents. First, by analyzing the limitations of current HITSbased algorithms, we propose a new weighted HITSbased method that assigns appropriate weights to inlinks of root documents. Then, we combine content analysis with HITSbased algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITSbased method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITSbased method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITSbased method. Between the four relevance scoring methods, there is no significant quality difference when they are combined with a HITSbased algorithm.
Inhalt: Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
Themenfeld: Suchmaschinen
Objekt: HITS