Search (21 results, page 1 of 2)

0.031738512 = product of:
  0.047607765 = sum of:
    0.03901083 = weight(_text_:im in 4193) [ClassicSimilarity], result of:
      0.03901083 = score(doc=4193,freq=6.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.27047595 = fieldWeight in 4193, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4193)
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 4193) [ClassicSimilarity], result of:
          0.025790809 = score(doc=4193,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 4193, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4193)
      0.33333334 = coord(1/3)
  0.6666667 = coord(2/3)

Abstract

Dieses Buch stellt Methoden der Begrifflichen Wissensverarbeitung vor und präsentiert Anwendungen aus unterschiedlichen Praxisfeldern. Im Methodenteil wird in moderne Techniken der Begrifflichen Datenanalyse und Wissensverarbeitung eingeführt. Der zweite Teil richtet sich verstärkt an potentielle Anwender. An ausgewählten Anwendungen wird die Vorgehensweise bei der Datenanalyse und dem Information Retrieval mit den Methoden der Begrifflichen Wissensverarbeitung vorgestellt und ihr Potential aufgezeigt

Content

Enthält die Beiträge: GANTER, B.: Begriffe und Implikationen; BURMEISTER, P.: ConImp: Ein Programm zur Fromalen Begriffsanalyse; Lengnink, K.: Ähnlichkeit als Distanz in Begriffsverbänden; POLLANDT, S.: Datenanalyse mit Fuzzy-Begriffen; PREDIGER, S.: Terminologische Merkmalslogik in der Formalen Begriffsanalyse; WILLE, R. u. M. ZICKWOLFF: Grundlagen einer Triadischen Begriffsanalyse; LINDIG, C. u. G. SNELTING: Formale Begriffsanalyse im Software Engineering; STRACK, H. u. M. SKORSKY: Zugriffskontrolle bei Programmsystemen und im Datenschutz mittels Formaler Begriffsanalyse; ANDELFINGER, U.: Inhaltliche Erschließung des Bereichs 'Sozialorientierte Gestaltung von Informationstechnik': Ein begriffsanalytischer Ansatz; GÖDERT, W.: Wissensdarstellung in Informationssystemen, Fragetypen und Anforderungen an Retrievalkomponenten; ROCK, T. u. R. WILLE: Ein TOSCANA-Erkundungssystem zur Literatursuche; ESCHENFELDER, D. u.a.: Ein Erkundungssystem zum Baurecht: Methoden der Entwicklung eines TOSCANA-Systems; GROßKOPF, A. u. G. HARRAS: Begriffliche Erkundung semantischer Strukturen von Sprechaktverben; ZELGER, J.: Grundwerte, Ziele und Maßnahmen in einem regionalen Krankenhaus: Eine Anwendung des Verfahrens GABEK; KOHLER-KOCH, B. u. F. VOGT: Normen- und regelgeleitete internationale Kooperationen: Formale Begriffsanalyse in der Politikwissenschaft; HENNING, H.J. u. W. KEMMNITZ: Entwicklung eines kontextuellen Methodenkonzeptes mit Hilfer der Formalen Begriffsanalyse an Beispielen zum Risikoverständnis; BARTEL, H.-G.: Über Möglichkeiten der Formalen Begriffsanalyse in der Mathematischen Archäochemie

0.025343753 = product of:
  0.07603126 = sum of:
    0.07603126 = product of:
      0.11404689 = sum of:
        0.058743894 = weight(_text_:online in 1901) [ClassicSimilarity], result of:
          0.058743894 = score(doc=1901,freq=4.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.37936267 = fieldWeight in 1901, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0625 = fieldNorm(doc=1901)
        0.055302992 = weight(_text_:22 in 1901) [ClassicSimilarity], result of:
          0.055302992 = score(doc=1901,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = queryNorm
            0.30952093 = fieldWeight in 1901, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1901)
      0.6666667 = coord(2/3)
  0.33333334 = coord(1/3)

Abstract

TOSCANA is a computer program which allows an online interaction with larger data bases to analyse and explore data conceptually. It uses labelled line diagrams of concept lattices to communicate knowledge coded in given data. The basic problem to create online presentations of concept lattices is solved by composing prepared diagrams to nested line diagrams. A larger number of applications in different areas have already shown that TOSCANA is a useful tool for many purposes

Source

Knowledge organization. 22(1995) no.2, S.78-81

0.021021385 = product of:
  0.06306415 = sum of:
    0.06306415 = weight(_text_:im in 3147) [ClassicSimilarity], result of:
      0.06306415 = score(doc=3147,freq=2.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.4372462 = fieldWeight in 3147, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.109375 = fieldNorm(doc=3147)
  0.33333334 = coord(1/3)

Footnote

Erscheint im Tagungsband der 16. Jahrestagung der Gesellschaft für Klassifikation 1992 in Dortmund

0.021021385 = product of:
  0.06306415 = sum of:
    0.06306415 = weight(_text_:im in 4199) [ClassicSimilarity], result of:
      0.06306415 = score(doc=4199,freq=2.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.4372462 = fieldWeight in 4199, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.109375 = fieldNorm(doc=4199)
  0.33333334 = coord(1/3)

0.018777166 = product of:
  0.056331497 = sum of:
    0.056331497 = product of:
      0.08449724 = sum of:
        0.03610713 = weight(_text_:retrieval in 5095) [ClassicSimilarity], result of:
          0.03610713 = score(doc=5095,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.23394634 = fieldWeight in 5095, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5095)
        0.048390117 = weight(_text_:22 in 5095) [ClassicSimilarity], result of:
          0.048390117 = score(doc=5095,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = queryNorm
            0.2708308 = fieldWeight in 5095, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5095)
      0.6666667 = coord(2/3)
  0.33333334 = coord(1/3)

Abstract

This paper presents an abstract formalization of the notion of "facets". Facets are relational structures of units, relations and other facets selected for a certain purpose. Facets can be used to structure large knowledge representation systems into a hierarchical arrangement of consistent and independent subsystems (facets) that facilitate flexibility and combinations of different viewpoints or aspects. This paper describes the basic notions, facet characteristics and construction mechanisms. It then explicates the theory in an example of a faceted information retrieval system (FaIR)

Date

22. 1.2016 17:47:06

0.018777166 = product of:
  0.056331497 = sum of:
    0.056331497 = product of:
      0.08449724 = sum of:
        0.03610713 = weight(_text_:retrieval in 2654) [ClassicSimilarity], result of:
          0.03610713 = score(doc=2654,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.23394634 = fieldWeight in 2654, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2654)
        0.048390117 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.048390117 = score(doc=2654,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = queryNorm
            0.2708308 = fieldWeight in 2654, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2654)
      0.6666667 = coord(2/3)
  0.33333334 = coord(1/3)

Abstract

Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.

Date

22. 1.2016 17:30:31

0.01801833 = product of:
  0.054054987 = sum of:
    0.054054987 = weight(_text_:im in 4200) [ClassicSimilarity], result of:
      0.054054987 = score(doc=4200,freq=2.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.37478244 = fieldWeight in 4200, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.09375 = fieldNorm(doc=4200)
  0.33333334 = coord(1/3)

0.014864365 = product of:
  0.044593092 = sum of:
    0.044593092 = weight(_text_:im in 5255) [ClassicSimilarity], result of:
      0.044593092 = score(doc=5255,freq=4.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.30917975 = fieldWeight in 5255, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5255)
  0.33333334 = coord(1/3)

Abstract

Begriffliche Datensysteme sind im Rahmen der Formalen Begriffsanalyse entstanden und gründen sich auf mathematische Formalisierungen von Begriff, Begriffssystem und Begriffliche Datei. Sie machen Wissen, das in einer Datenbasis vorliegt, begrifflich zugänglich und interpretierbar. Hierfür werden begriffliche Zusammenhänge entsprechend gewählter Frageaspekte in gestuften Liniendiagrammen dargestellt. Durch Verfeinern, Vergröbern und Wechseln von Begriffstrukturen kann man unbegrenzt durch das in der Datenbasis gespeicherte Wissen "navigieren". In einem Forschungsprojekt, gefördert vom Zentrum für interdisziplinäre Technikforschung an der TH Darmstadt, ist ein Prototyp eines begrifflichen Datensystems erstellt worden, dem als Datenkontext eine ausgewählte, begrifflich aufgearbeitete Menge von Büchern zur interdisziplinären Technikforschung zugrunde liegt. Mit diesem Prototyp soll die flexible und variable Verwendung begrifflicher datensysteme im Literaturbereich demonstriert werden

0.01201222 = product of:
  0.03603666 = sum of:
    0.03603666 = weight(_text_:im in 3039) [ClassicSimilarity], result of:
      0.03603666 = score(doc=3039,freq=2.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.24985497 = fieldWeight in 3039, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.0625 = fieldNorm(doc=3039)
  0.33333334 = coord(1/3)

Abstract

Ausgeführt wird, wie Begriffsverbände als Ablaufschemata zur Gegenstandsbestimmung genutzt werden können. Im Gegensatz zur Baumabfrage gestattet die beschriebene Methode dem Benutzer ein Höchstmaß an Freiheit und Transparenz. Demonstriert wird die Methode an der Bestimmung des Symmetrieprinzips von Flächenmustern

0.01201222 = product of:
  0.03603666 = sum of:
    0.03603666 = weight(_text_:im in 585) [ClassicSimilarity], result of:
      0.03603666 = score(doc=585,freq=2.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.24985497 = fieldWeight in 585, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.0625 = fieldNorm(doc=585)
  0.33333334 = coord(1/3)

Abstract

TOSCANA ist ein Computerprogramm, mit dem begriffliche Erkundungssysteme auf der Grundlage der Formalen Begriffsanalyse erstellt werden können.In der vorliegenden Arbeit wird diskutiert, wie TOSCANA zur bibliothekarischen Sacherschließung und thematischen Literatursuche eingesetzt werden kann. Berichtet wird dabei von dem Forschungsprojekt 'Anwendung eines Modells begrifflicher Wissenssysteme im Bereich der Literatur zur interdisziplinären Technikforschung', das vom Darmstädter Zentrum für interdisziplinäre Technikforschung gefördert worden ist

0.01201222 = product of:
  0.03603666 = sum of:
    0.03603666 = weight(_text_:im in 4376) [ClassicSimilarity], result of:
      0.03603666 = score(doc=4376,freq=2.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.24985497 = fieldWeight in 4376, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.0625 = fieldNorm(doc=4376)
  0.33333334 = coord(1/3)

Abstract

Ein grundsätzliches Problem der Literaturverwaltung besteht darin, daß viele Nutzer der Retrievalsysteme gar nicht genau sagen können, was sie suchen. Erst im Prozeß des erkundenden Suchens lernen sie genauer zu präzisieren, was sie finden wollen. Dieser Lernprozeß wird durch einzelne Suchwörter (Suchwortketten) nur unzureichend unterstützt, weshalb der benutzer häufig unzufrieden mit dem Ergebnis eines solchen Suchprozesses ist. Notwendig sind reichhaltigere Begriffsnetze, die thematisch geordnete Zusammenhänge darstellen und sich flexibel verfeinern, vergröbern oder verändern lassen, um in geeignetem Umfang die wünschenswerte Orientierung liefern zu können. Das Computerprogramm TOSCANA könnte hier weiterhelfen

0.010617403 = product of:
  0.031852208 = sum of:
    0.031852208 = weight(_text_:im in 4390) [ClassicSimilarity], result of:
      0.031852208 = score(doc=4390,freq=4.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.22084267 = fieldWeight in 4390, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4390)
  0.33333334 = coord(1/3)

Abstract

Die Kommunikation von Wissen nimmt in der modernen Wissensgesellschaft einen entscheidenden Stellenwert ein. Kommunikation im Habermas'schen Sinne eines intersubjektiven Verständigungsprozesses ist dann aber auch mehr als nur der Austausch von Zeichen: es geht um Sinn und Bedeutung und die Aushandlungsprozesse darüber, wie wir als Kommunikationsgemeinschaft Zeichen interpretieren und darin Informationen codieren. Als Medium für solche Kommunikations - prozesse eignen sich besonders gut Liniendiagramme aus der Theorie der Formalen Begriffsanalyse. Diese Liniendiagramme sind nicht nur geeignet, die Wissenskommunikation zu unterstützen, sondern auch Kommunikationsprozesse überhaupt erst zu initiieren. Solche Liniendiagramme können die Wissenskommunikation gut unterstützen, da sie durch ihre Einfachheit, Ordnung, Prägnanz und ergänzende Stimulanz für Verständigung über die wissensgenerierende Information sorgen. Außerdem wird mit den Liniendiagrammen ein Kommunikationsmittel bereitgestellt, dass inter- und transdisziplinär wirksam werden kann und so Wissensgebiete für verschiedene Disziplinen erschließt, da es mit den Diagrammen gelingt, die allgemeine, zugrundeliegende logische Struktur mit Hilfe eines mathematisch fundierten Verfahrens herauszuarbeiten. Liniendiagramme stellen nicht nur Wissensgebiete in einer geordneten, strukturierten Form dar, sondern verwenden dafür auch formale Begriffe und knüpfen damit an Begriffe als Objekte des menschlichen Denkens an. In den Begriffe verschmilzt ein Ausschnitt der betrachteten Objekte (im Beispiel die verschiedenen Gewässerarten) mit den ihnen gemeinsamen Merkmalen zu neuen Denkeinheiten und geben somit dem Wissen eine Form, in der Kommunikation über diese Denkeinheiten und die darin konzentrierte Information ermöglicht wird.

0.007680971 = product of:
  0.023042914 = sum of:
    0.023042914 = product of:
      0.06912874 = sum of:
        0.06912874 = weight(_text_:22 in 3142) [ClassicSimilarity], result of:
          0.06912874 = score(doc=3142,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = queryNorm
            0.38690117 = fieldWeight in 3142, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3142)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

26. 2.2008 15:58:22

0.006948821 = product of:
  0.020846462 = sum of:
    0.020846462 = product of:
      0.062539384 = sum of:
        0.062539384 = weight(_text_:retrieval in 6055) [ClassicSimilarity], result of:
          0.062539384 = score(doc=6055,freq=6.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.40520695 = fieldWeight in 6055, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6055)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

A lattice-based model for information retrieval was suggested in the 1960's but has been seen as a theoretical possibility hard to practically apply ever since. This paper attempts to revive the lattice model and demonstrate its applicability in an information retrieval system, FalR, that incorporates a graphical representation of a faceted thesaurus. It shows how Boolean queries can be lattice-theoretically related to the concepts of the thesaurus and visualized within the thesaurus display. An advantage of FaIR is that it allows for a high level of transparency of the system, which can be controlled by the user

0.005956133 = product of:
  0.017868398 = sum of:
    0.017868398 = product of:
      0.05360519 = sum of:
        0.05360519 = weight(_text_:retrieval in 2737) [ClassicSimilarity], result of:
          0.05360519 = score(doc=2737,freq=6.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.34732026 = fieldWeight in 2737, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=2737)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.

0.005673688 = product of:
  0.017021064 = sum of:
    0.017021064 = product of:
      0.05106319 = sum of:
        0.05106319 = weight(_text_:retrieval in 2710) [ClassicSimilarity], result of:
          0.05106319 = score(doc=2710,freq=4.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.33085006 = fieldWeight in 2710, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2710)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Latent Semantic Indexing (LSI), a variant of classical Vector Space Model (VSM), is an Information Retrieval (IR) model that attempts to capture the latent semantic relationship between the data items. Mathematical lattices, under the framework of Formal Concept Analysis (FCA), represent conceptual hierarchies in data and retrieve the information. However both LSI and FCA uses the data represented in form of matrices. The objective of this paper is to systematically analyze VSM, LSI and FCA for the task of IR using the standard and real life datasets.

0.004585033 = product of:
  0.013755098 = sum of:
    0.013755098 = product of:
      0.041265294 = sum of:
        0.041265294 = weight(_text_:retrieval in 2204) [ClassicSimilarity], result of:
          0.041265294 = score(doc=2204,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.26736724 = fieldWeight in 2204, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=2204)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

With the continuously growing amount of Internet accessible information resources, locating relevant information in the WWW becomes increasingly difficult. Recent developments provide scalable mechanisms for maintaing indexes of network accessible information. In order to implement sophisticated retrieval engines, a means of automatic analysis and classification of document meta information has to be found. Proposes the use of methods from the mathematical theory of concept analysis to analyze and interactively explore the information space defined by wide area resource discovery services

0.004585033 = product of:
  0.013755098 = sum of:
    0.013755098 = product of:
      0.041265294 = sum of:
        0.041265294 = weight(_text_:retrieval in 3291) [ClassicSimilarity], result of:
          0.041265294 = score(doc=3291,freq=8.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.26736724 = fieldWeight in 3291, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=3291)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

The web is a network of linked sites whereby each site either forms a physical portal or a standalone page. In the former case, the portal presents an access point to its embedded web pages that coherently present a specific topic. In the latter case, there are millions of standalone web pages, that are scattered throughout the web, having the same topic and could be conceptually linked together to form virtual portals. Search engines have been developed to help users in reaching the adequate pages in an efficient and effective manner. All the known current search engine techniques rely on the web page as the basic atomic search unit. They ignore the conceptual links, that reveal the implicit web related meanings, among the retrieved pages. However, building a semantic model for the whole portal may contain more semantic information than a model of scattered individual pages. In addition, user queries can be poor and contain imprecise terms that do not reflect the real user intention. Consequently, retrieving the standalone individual pages that are directly related to the query may not satisfy the user's need. In this paper, we propose PREFCA, a Portal Retrieval Engine based on Formal Concept Analysis that relies on the portal as the main search unit. PREFCA consists of three phases: First, the information extraction phase that is concerned with extracting portal's semantic data. Second, the formal concept analysis phase that utilizes formal concept analysis to discover the conceptual links among portal and attributes. Finally, the information retrieval phase where we propose a portal ranking method to retrieve ranked pairs of portals and embedded pages. Additionally, we apply the network analysis rules to output some portal characteristics. We evaluated PREFCA using two data sets, namely the Forum for Information Retrieval Evaluation 2010 and ClueWeb09 (category B) test data, for physical and virtual portals respectively. PREFCA proves higher F-measure accuracy, better Mean Average Precision ranking and comparable network analysis and efficiency results than other search engine approaches, namely Term Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), and BM25 techniques. As well, it gains high Mean Average Precision in comparison with learning to rank techniques. Moreover, PREFCA also gains better reach time than Carrot as a well-known topic-based search engine.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 4766) [ClassicSimilarity], result of:
          0.025790809 = score(doc=4766,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 4766, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4766)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Current best-match ranking (BMR) systems perform well but cannot handle word mismatch between a query and a document. The best known alternative ranking method, hierarchical clustering-based ranking (HCR), seems to be more robust than BMR with respect to this problem, but it is hampered by theoretical and practical limitations. We present an approach to document ranking that explicitly addresses the word mismatch problem by exploiting interdocument similarity information in a novel way. Document ranking is seen as a query-document transformation driven by a conceptual representation of the whole document collection, into which the query is merged. Our approach is nased on the theory of concept (or Galois) lattices, which, er argue, provides a powerful, well-founded, and conputationally-tractable framework to model the space in which documents and query are represented and to compute such a transformation. We compared information retrieval using concept lattice-based ranking (CLR) to BMR and HCR. The results showed that HCR was outperformed by CLR as well as BMR, and suggested that, of the two best methods, BMR achieved better performance than CLR on the whole document set, whereas CLR compared more favorably when only the first retrieved documents were used for evaluation. We also evaluated the three methods' specific ability to rank documents that did not match the query, in which case the speriority of CLR over BMR and HCR was apparent

0.002307678 = product of:
  0.006923034 = sum of:
    0.006923034 = product of:
      0.0207691 = sum of:
        0.0207691 = weight(_text_:online in 2470) [ClassicSimilarity], result of:
          0.0207691 = score(doc=2470,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.13412495 = fieldWeight in 2470, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.03125 = fieldNorm(doc=2470)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

The writers show that a faceted navigation structure makes web sites easier to use. They begin by analyzing the web sites of three library and information science faculties, and seeing if the sites easily provide the answers to five specific questions, e.g., how the school ranks in national evaluations. (It is worth noting that the web site of the Faculty of Information Studies and the University of Toronto, where this bibliography is being written, would fail on four of the five questions.) Using examples from LIS web site content, they show how facets can be related and constructed, and use concept diagrams for illustration. They briefly discuss constraints necessary when joining facets: for example, enrolled students can be full- or part-time, but prospective and alumni students cannot. It should not be possible to construct terms such as "part-time alumni" (see Yannis Tzitzikas et al, below in Background). They conclude that a faceted approach is best for web site navigation, because it can clearly show where the user is in the site, what the related pages are, and how to get to them. There is a short discussion of user interfaces, and the diagrams in the paper will be of interest to anyone making a facet-based web site. This paper is clearly written, informative, and thought-provoking. Uta Priss's web site lists her other publications, many of which are related and some of which are online: http://www.upriss.org.uk/top/research.html.