Search (16 results, page 1 of 1)

  • × theme_ss:"Retrievalalgorithmen"
  • × year_i:[2000 TO 2010}
  1. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.02 
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    Date
    25. 8.2005 17:42:22
  2. Watters, C.; Amoudi, A.: Geosearcher : location-based ranking of search engine results (2003) 0.02 
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    Abstract
    Waters and Amoudi describe GeoSearcher, a prototype ranking program that arranges search engine results along a geo-spatial dimension without the provision of geo-spatial meta-tags or the use of geo-spatial feature extraction. GeoSearcher uses URL analysis, IptoLL, Whois, and the Getty Thesaurus of Geographic Names to determine site location. It accepts the first 200 sites returned by a search engine, identifies the coordinates, calculates their distance from a reference point and ranks in ascending order by this value. For any retrieved site the system checks if it has already been located in the current session, then sends the domain name to Whois to generate a return of a two letter country code and an area code. With no success the name is stripped one level and resent. If this fails the top level domain is tested for being a country code. Any remaining unmatched names go to IptoLL. Distance is calculated using the center point of the geographic area and a provided reference location. A test run on a set of 100 URLs from a search was successful in locating 90 sites. Eighty three pages could be manually found and 68 had sufficient information to verify location determination. Of these 65 ( 95%) had been assigned reasonably correct geographic locations. A random set of URLs used instead of a search result, yielded 80% success.
  3. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22
  4. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01 
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    Date
    22. 3.2003 19:27:23
  5. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01 
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    Date
    20. 3.2005 16:23:22
  6. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.01 
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    Date
    22. 3.2003 19:27:36
  7. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.01 
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    Date
    31. 5.2004 19:22:06
  8. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.01 
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    Date
    11. 9.2004 17:32:22
  9. Witschel, H.F.: Global term weights in distributed environments (2008) 0.01 
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    Date
    1. 8.2008 9:44:22
  10. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.01 
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    Date
    16.11.2008 16:22:48
  11. Campos, L.M. de; Fernández-Luna, J.M.; Huete, J.F.: Implementing relevance feedback in the Bayesian network retrieval model (2003) 0.01 
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    Date
    22. 3.2003 19:30:19
  12. Mandl, T.: Tolerantes Information Retrieval : Neuronale Netze zur Erhöhung der Adaptivität und Flexibilität bei der Informationssuche (2001) 0.01 
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    Footnote
    da nun nach fast 200 Seiten der Hauptteil der Dissertation folgt - die Vorstellung und Bewertung des bereits erwähnten COSIMIR Modells. Das COSIMIR Modell "berechnet die Ähnlichkeit zwischen den zwei anliegenden Input-Vektoren" (P.194). Der Output des Netzwerks wird an einem einzigen Knoten abgegriffen, an dem sich ein sogenannten Relevanzwert einstellt, wenn die Berechnungen der Gewichtungen interner Knoten zum Abschluss kommen. Diese Gewichtungen hängen von den angelegten Inputvektoren, aus denen die Gewichte der ersten Knotenschicht ermittelt werden, und den im Netzwerk vorgegebenen Kantengewichten ab. Die Gewichtung von Kanten ist der Kernpunkt des neuronalen Ansatzes: In Analogie zum biologischen Urbild (Dendrit mit Synapsen) wächst das Gewicht der Kante mit jeder Aktivierung während einer Trainingsphase. Legt man in dieser Phase zwei Inputvektoren, z.B. Dokumentvektor und Ouery gleichzeitig mit dem Relevanzurteil als Wert des Outputknoten an, verteilen sich durch den BackpropagationProzess die Gewichte entlang der Pfade, die zwischen den beteiligten Knoten bestehen. Da alle Knoten miteinander verbunden sind, entstehen nach mehreren Trainingsbeispielen bereits deutlich unterschiedliche Kantengewichte, weil die aktiv beteiligten Kanten die Änderungen akkumulativ speichern. Eine Variation des Verfahrens benutzt das NN als "Transformationsnetzwerk", wobei die beiden Inputvektoren mit einer Dokumentrepräsentation und einem dazugehörigen Indexat (von einem Experten bereitgestellt) belegt werden. Neben der schon aufgezeigten Trainingsnotwendigkeit weisen die Neuronalen Netze eine weitere intrinsische Problematik auf: Je mehr äußere Knoten benötigt werden, desto mehr interne Kanten (und bei der Verwendung von Zwischenschichten auch Knoten) sind zu verwalten, deren Anzahl nicht linear wächst. Dieser algorithmische Befund setzt naiven Einsätzen der NN-Modelle in der Praxis schnell Grenzen, deshalb ist es umso verdienstvoller, dass der Autor einen innovativen Weg zur Lösung des Problems mit den Mitteln des IR vorschlagen kann. Er verwendet das Latent Semantic Indexing, welches Dokumentrepräsentationen aus einem hochdimensionalen Vektorraum in einen niederdimensionalen abbildet, um die Anzahl der Knoten deutlich zu reduzieren. Damit ist eine sehr schöne Synthese gelungen, welche die eingangs angedeuteten formalen Übereinstimmungen zwischen Vektorraummodellen im IR und den NN aufzeigt und ausnutzt.
  13. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.01 
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    Date
    22. 3.2003 19:35:46
  14. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.01 
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    Date
    22. 7.2006 16:32:43
  15. Dominich, S.: Mathematical foundations of information retrieval (2001) 0.01 
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    Date
    22. 3.2008 12:26:32
  16. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.01 
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    Source
    Electronic library. 22(2004) no.2, S.112-120

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