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Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
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- 25. 8.2005 17:42:22
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Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
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- Date
- 14. 6.2015 22:12:44
- Source
- Deutscher Dokumentartag 1985, Nürnberg, 1.-4.10.1985: Fachinformation: Methodik - Management - Markt; neue Entwicklungen, Berufe, Produkte. Bearb.: H. Strohl-Goebel
-
Bar-Ilan, J.; Levene, M.: ¬The hw-rank : an h-index variant for ranking web pages (2015)
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-
Na, S.-H.; Kang, I.-S.; Roh, J.-E.; Lee, J.-H.: ¬An empirical study of query expansion and cluster-based retrieval in language modeling approach (2007)
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-
Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003)
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- Date
- 11. 9.2004 17:32:22
-
Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998)
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- Date
- 1. 8.1996 22:08:06
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Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
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- Date
- 20. 3.2005 16:23:22
- Source
- Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
-
Lee, J.-T.; Seo, J.; Jeon, J.; Rim, H.-C.: Sentence-based relevance flow analysis for high accuracy retrieval (2011)
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Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003)
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- Source
- Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 56(2003) H.3/4, S.49-63
-
Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009)
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- Abstract
- This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
-
Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004)
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- Date
- 31. 5.2004 19:22:06
-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Source
- Information processing and management. 22(1986) no.6, S.465-476
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- Date
- 30. 3.2001 13:32:22
-
Jiang, X.; Sun, X.; Yang, Z.; Zhuge, H.; Lapshinova-Koltunski, E.; Yao, J.: Exploiting heterogeneous scientific literature networks to combat ranking bias : evidence from the computational linguistics area (2016)
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Dadashkarimia, J.; Shakery, A.; Failia, H.; Zamani, H.: ¬An expectation-maximization algorithm for query translation based on pseudo-relevant documents (2017)
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-
Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986)
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- Date
- 14. 6.2015 22:12:56
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Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001)
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Ackermann, J.: Knuth-Morris-Pratt (2005)
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- Abstract
- Im Rahmen des Seminars Suchmaschinen und Suchalgorithmen beschäftigt sich diese Arbeit mit dem Auffinden bestimmter Wörter oder Muster in Texten. Der Begriff "Text" wird hier in einem sehr allgemeinen Sinne als strukturierte Folge beliebiger Länge von Zeichen aus einem endlichen Alphabet verstanden. Somit fällt unter diesen Bereich ganz allgemein die Suche nach einem Muster in einer Sequenz von Zeichen. Beispiele hierfür sind neben der Suche von Wörtern in "literarischen" Texten, z.B. das Finden von Pixelfolgen in Bildern oder gar das Finden von Mustern in DNS-Strängen. Das Anwendungsgebiet für eine solche Suche ist weit gefächert. Man denke hier allein an Texteditoren, Literaturdatenbanken, digitale Lexika oder die besagte DNADatenbank. Betrachtet man allein das 1989 publizierte Oxford English Dictionary mit seinen etwa 616500 definierten Stichworten auf gedruckten 21728 Seiten, so gilt es, einen möglichst effizienten Algorithmus für die Suche in Texten zu nutzen. Der in der Arbeit zugrunde liegende Datentyp ist vom Typ String (Zeichenkette), wobei hier offen gelassen wird, wie der Datentyp programmtechnisch realisiert wird. Algorithmen zur Verarbeitung von Zeichenketten (string processing) umfassen ein bestimmtes Spektrum an Anwendungsgebieten [Ot96, S.617 f.], wie z.B. das Komprimieren, das Verschlüssen, das Analysieren (parsen), das Übersetzen von Texten sowie das Suchen in Texten, welches Thema dieses Seminars ist. Im Rahmen dieser Arbeit wird der Knuth-Morris-Pratt Algorithmus vorgestellt, der wie der ebenfalls in diesem Seminar vorgestellte Boyer-Moore Algorithmus einen effizienten Suchalgorithmus darstellt. Dabei soll ein gegebenes Suchwort oder Muster (pattern) in einer gegeben Zeichenkette erkannt werden (pattern matching). Gesucht werden dabei ein oder mehrere Vorkommen eines bestimmten Suchwortes (exact pattern matching). Der Knuth-Morris-Pratt Algorithmus wurde erstmals 1974 als Institutbericht der Stanford University beschrieben und erschien 1977 in der Fachzeitschrift Journal of Computing unter dem Titel "Fast Pattern Matching in Strings" [Kn77]. Der Algorithmus beschreibt eine Suche in Zeichenketten mit linearer Laufzeit. Der Name des Algorithmus setzt sich aus den Entwicklern des Algorithmus Donald E. Knuth, James H. Morris und Vaughan R. Pratt zusammen.
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Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Xiangji Huang, J.; Ben Jemaa, M.: MF-Re-Rank : a modality feature-based re-ranking model for medical image retrieval (2018)
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Savoy, J.; Ndarugendamwo, M.; Vrajitoru, D.: Report on the TREC-4 experiment : combining probabilistic and vector-space schemes (1996)
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