Search (134 results, page 1 of 7)

0.23271671 = product of:
  0.46543342 = sum of:
    0.06154665 = product of:
      0.18463995 = sum of:
        0.18463995 = weight(_text_:3a in 400) [ClassicSimilarity], result of:
          0.18463995 = score(doc=400,freq=2.0), product of:
            0.32853028 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03875087 = queryNorm
            0.56201804 = fieldWeight in 400, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=400)
      0.33333334 = coord(1/3)
    0.034606863 = weight(_text_:work in 400) [ClassicSimilarity], result of:
      0.034606863 = score(doc=400,freq=2.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.2433148 = fieldWeight in 400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.046875 = fieldNorm(doc=400)
    0.18463995 = weight(_text_:2f in 400) [ClassicSimilarity], result of:
      0.18463995 = score(doc=400,freq=2.0), product of:
        0.32853028 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03875087 = queryNorm
        0.56201804 = fieldWeight in 400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=400)
    0.18463995 = weight(_text_:2f in 400) [ClassicSimilarity], result of:
      0.18463995 = score(doc=400,freq=2.0), product of:
        0.32853028 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03875087 = queryNorm
        0.56201804 = fieldWeight in 400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=400)
  0.5 = coord(4/8)

Abstract

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-to-descendent link with a specific facet "type-of". We use information extraction techniques to find synonyms, sibling concepts, and ancestor-descendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy.

Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

0.17396295 = product of:
  0.3479259 = sum of:
    0.0410311 = product of:
      0.1230933 = sum of:
        0.1230933 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
          0.1230933 = score(doc=701,freq=2.0), product of:
            0.32853028 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03875087 = queryNorm
            0.3746787 = fieldWeight in 701, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=701)
      0.33333334 = coord(1/3)
    0.060708184 = weight(_text_:cooperative in 701) [ClassicSimilarity], result of:
      0.060708184 = score(doc=701,freq=2.0), product of:
        0.23071818 = queryWeight, product of:
          5.953884 = idf(docFreq=311, maxDocs=44218)
          0.03875087 = queryNorm
        0.263127 = fieldWeight in 701, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.953884 = idf(docFreq=311, maxDocs=44218)
          0.03125 = fieldNorm(doc=701)
    0.1230933 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
      0.1230933 = score(doc=701,freq=2.0), product of:
        0.32853028 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03875087 = queryNorm
        0.3746787 = fieldWeight in 701, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=701)
    0.1230933 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
      0.1230933 = score(doc=701,freq=2.0), product of:
        0.32853028 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03875087 = queryNorm
        0.3746787 = fieldWeight in 701, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=701)
  0.5 = coord(4/8)

Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

0.14594682 = product of:
  0.3891915 = sum of:
    0.0410311 = product of:
      0.1230933 = sum of:
        0.1230933 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
          0.1230933 = score(doc=5820,freq=2.0), product of:
            0.32853028 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03875087 = queryNorm
            0.3746787 = fieldWeight in 5820, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
      0.33333334 = coord(1/3)
    0.17408021 = weight(_text_:2f in 5820) [ClassicSimilarity], result of:
      0.17408021 = score(doc=5820,freq=4.0), product of:
        0.32853028 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03875087 = queryNorm
        0.5298757 = fieldWeight in 5820, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=5820)
    0.17408021 = weight(_text_:2f in 5820) [ClassicSimilarity], result of:
      0.17408021 = score(doc=5820,freq=4.0), product of:
        0.32853028 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03875087 = queryNorm
        0.5298757 = fieldWeight in 5820, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=5820)
  0.375 = coord(3/8)

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

0.13350022 = product of:
  0.35600057 = sum of:
    0.14865705 = weight(_text_:supported in 1566) [ClassicSimilarity], result of:
      0.14865705 = score(doc=1566,freq=4.0), product of:
        0.22949564 = queryWeight, product of:
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.03875087 = queryNorm
        0.64775544 = fieldWeight in 1566, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1566)
    0.15024509 = weight(_text_:cooperative in 1566) [ClassicSimilarity], result of:
      0.15024509 = score(doc=1566,freq=4.0), product of:
        0.23071818 = queryWeight, product of:
          5.953884 = idf(docFreq=311, maxDocs=44218)
          0.03875087 = queryNorm
        0.6512061 = fieldWeight in 1566, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.953884 = idf(docFreq=311, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1566)
    0.057098407 = weight(_text_:work in 1566) [ClassicSimilarity], result of:
      0.057098407 = score(doc=1566,freq=4.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.40144894 = fieldWeight in 1566, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1566)
  0.375 = coord(3/8)

Classification

ST 285 Informatik / Monographien / Software und -entwicklung / Computer supported cooperative work (CSCW), Groupware

RVK

ST 285 Informatik / Monographien / Software und -entwicklung / Computer supported cooperative work (CSCW), Groupware

0.09879459 = product of:
  0.26345223 = sum of:
    0.10618362 = weight(_text_:supported in 1529) [ClassicSimilarity], result of:
      0.10618362 = score(doc=1529,freq=4.0), product of:
        0.22949564 = queryWeight, product of:
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.03875087 = queryNorm
        0.4626825 = fieldWeight in 1529, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1529)
    0.10731792 = weight(_text_:cooperative in 1529) [ClassicSimilarity], result of:
      0.10731792 = score(doc=1529,freq=4.0), product of:
        0.23071818 = queryWeight, product of:
          5.953884 = idf(docFreq=311, maxDocs=44218)
          0.03875087 = queryNorm
        0.4651472 = fieldWeight in 1529, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.953884 = idf(docFreq=311, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1529)
    0.049950704 = weight(_text_:work in 1529) [ClassicSimilarity], result of:
      0.049950704 = score(doc=1529,freq=6.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.35119468 = fieldWeight in 1529, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1529)
  0.375 = coord(3/8)

Abstract

The aim of this book is to highlight the relationship between knowledge representation and language in artificial intelligence, and in particular on the way in which the choice of representation influences the language used to discuss a problem - and vice versa. Opening with a discussion of knowledge representation methods, and following this with a look at reasoning methods, the author begins to make his case for the intimate relationship between language and representation. He shows how each representation method fits particularly well with some reasoning methods and less so with others, using specific languages as examples. The question of representation change, an important and complex issue about which very little is known, is addressed. Dr Hodgson gathers together recent work on problem solving, showing how, in some cases, it has been possible to use representation changes to recast problems into a language that makes them easier to solve. The author maintains throughout that the relationships that this book explores lie at the heart of the construction of large systems, examining a number of the current large AI systems from the viewpoint of representation and language to prove his point.

Classification

ST 285 Informatik / Monographien / Software und -entwicklung / Computer supported cooperative work (CSCW), Groupware

RVK

ST 285 Informatik / Monographien / Software und -entwicklung / Computer supported cooperative work (CSCW), Groupware

0.032595918 = product of:
  0.13038367 = sum of:
    0.112007946 = weight(_text_:hochschule in 1852) [ClassicSimilarity], result of:
      0.112007946 = score(doc=1852,freq=2.0), product of:
        0.23689921 = queryWeight, product of:
          6.113391 = idf(docFreq=265, maxDocs=44218)
          0.03875087 = queryNorm
        0.47280845 = fieldWeight in 1852, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.113391 = idf(docFreq=265, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1852)
    0.018375728 = product of:
      0.036751457 = sum of:
        0.036751457 = weight(_text_:22 in 1852) [ClassicSimilarity], result of:
          0.036751457 = score(doc=1852,freq=2.0), product of:
            0.13569894 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03875087 = queryNorm
            0.2708308 = fieldWeight in 1852, 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=1852)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.

Date

11. 2.2011 18:22:58

0.032595918 = product of:
  0.13038367 = sum of:
    0.112007946 = weight(_text_:hochschule in 4324) [ClassicSimilarity], result of:
      0.112007946 = score(doc=4324,freq=2.0), product of:
        0.23689921 = queryWeight, product of:
          6.113391 = idf(docFreq=265, maxDocs=44218)
          0.03875087 = queryNorm
        0.47280845 = fieldWeight in 4324, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.113391 = idf(docFreq=265, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4324)
    0.018375728 = product of:
      0.036751457 = sum of:
        0.036751457 = weight(_text_:22 in 4324) [ClassicSimilarity], result of:
          0.036751457 = score(doc=4324,freq=2.0), product of:
            0.13569894 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03875087 = queryNorm
            0.2708308 = fieldWeight in 4324, 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=4324)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.

Date

11. 2.2011 18:22:25

0.031417284 = product of:
  0.12566914 = sum of:
    0.09106228 = weight(_text_:cooperative in 2737) [ClassicSimilarity], result of:
      0.09106228 = score(doc=2737,freq=2.0), product of:
        0.23071818 = queryWeight, product of:
          5.953884 = idf(docFreq=311, maxDocs=44218)
          0.03875087 = queryNorm
        0.3946905 = fieldWeight in 2737, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.953884 = idf(docFreq=311, maxDocs=44218)
          0.046875 = fieldNorm(doc=2737)
    0.034606863 = weight(_text_:work in 2737) [ClassicSimilarity], result of:
      0.034606863 = score(doc=2737,freq=2.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.2433148 = fieldWeight in 2737, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.046875 = fieldNorm(doc=2737)
  0.25 = coord(2/8)

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.

0.022629023 = product of:
  0.18103218 = sum of:
    0.18103218 = weight(_text_:hochschule in 4499) [ClassicSimilarity], result of:
      0.18103218 = score(doc=4499,freq=4.0), product of:
        0.23689921 = queryWeight, product of:
          6.113391 = idf(docFreq=265, maxDocs=44218)
          0.03875087 = queryNorm
        0.76417387 = fieldWeight in 4499, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          6.113391 = idf(docFreq=265, maxDocs=44218)
          0.0625 = fieldNorm(doc=4499)
  0.125 = coord(1/8)

Content

Diplomarbeit zur Erlangung des Grades eines Diplom-Wirtschaftsinformatikers (FH) der Hochschule Wismar. Vgl.: http://www.wi.hs-wismar.de/~cleve/vorl/projects/da/DA-FS-Haas.pdf.

Imprint

Wismar : Hochschule Wismar; Fachbereich Wirtschaft

0.022052169 = product of:
  0.088208675 = sum of:
    0.07508315 = weight(_text_:supported in 2645) [ClassicSimilarity], result of:
      0.07508315 = score(doc=2645,freq=2.0), product of:
        0.22949564 = queryWeight, product of:
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.03875087 = queryNorm
        0.3271659 = fieldWeight in 2645, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2645)
    0.01312552 = product of:
      0.02625104 = sum of:
        0.02625104 = weight(_text_:22 in 2645) [ClassicSimilarity], result of:
          0.02625104 = score(doc=2645,freq=2.0), product of:
            0.13569894 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03875087 = queryNorm
            0.19345059 = fieldWeight in 2645, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2645)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Major efforts have been conducted on ontology learning, that is, semiautomatic processes for the construction of domain ontologies from diverse sources of information. In the past few years, a research trend has focused on the construction of educational ontologies, that is, ontologies to be used for educational purposes. The identification of the terminology is crucial to build ontologies. Term extraction techniques allow the identification of the domain-related terms from electronic resources. This paper presents LiTeWi, a novel method that combines current unsupervised term extraction approaches for creating educational ontologies for technology supported learning systems from electronic textbooks. LiTeWi uses Wikipedia as an additional information source. Wikipedia contains more than 30 million articles covering the terminology of nearly every domain in 288 languages, which makes it an appropriate generic corpus for term extraction. Furthermore, given that its content is available in several languages, it promotes both domain and language independence. LiTeWi is aimed at being used by teachers, who usually develop their didactic material from textbooks. To evaluate its performance, LiTeWi was tuned up using a textbook on object oriented programming and then tested with two textbooks of different domains-astronomy and molecular biology.

Date

22. 1.2016 12:38:14

0.019800395 = product of:
  0.15840316 = sum of:
    0.15840316 = weight(_text_:hochschule in 5496) [ClassicSimilarity], result of:
      0.15840316 = score(doc=5496,freq=4.0), product of:
        0.23689921 = queryWeight, product of:
          6.113391 = idf(docFreq=265, maxDocs=44218)
          0.03875087 = queryNorm
        0.6686521 = fieldWeight in 5496, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          6.113391 = idf(docFreq=265, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5496)
  0.125 = coord(1/8)

Footnote

Master thesis Library and Information Science, Fakultät für Informations- und Kommunikationswissenschaften, Technische Hochschule Köln. Schön auch: Bei Google Scholar unter 'Eva, S.' nachgewiesen.

Imprint

Köln : Technische Hochschule / Fakultät für Informations- und Kommunikationswissenschaften

0.01617303 = product of:
  0.06469212 = sum of:
    0.048941493 = weight(_text_:work in 1652) [ClassicSimilarity], result of:
      0.048941493 = score(doc=1652,freq=4.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.3440991 = fieldWeight in 1652, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.046875 = fieldNorm(doc=1652)
    0.015750622 = product of:
      0.031501245 = sum of:
        0.031501245 = weight(_text_:22 in 1652) [ClassicSimilarity], result of:
          0.031501245 = score(doc=1652,freq=2.0), product of:
            0.13569894 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03875087 = queryNorm
            0.23214069 = fieldWeight in 1652, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1652)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

This document is the formal definition of the CIDOC Conceptual Reference Model ("CRM"), a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous cultural heritage information. The CRM is the culmination of more than a decade of standards development work by the International Committee for Documentation (CIDOC) of the International Council of Museums (ICOM). Work on the CRM itself began in 1996 under the auspices of the ICOM-CIDOC Documentation Standards Working Group. Since 2000, development of the CRM has been officially delegated by ICOM-CIDOC to the CIDOC CRM Special Interest Group, which collaborates with the ISO working group ISO/TC46/SC4/WG9 to bring the CRM to the form and status of an International Standard.

Date

6. 8.2010 14:22:28

0.01410795 = product of:
  0.1128636 = sum of:
    0.1128636 = product of:
      0.2257272 = sum of:
        0.2257272 = weight(_text_:aufsatzsammlung in 1952) [ClassicSimilarity], result of:
          0.2257272 = score(doc=1952,freq=12.0), product of:
            0.25424787 = queryWeight, product of:
              6.5610886 = idf(docFreq=169, maxDocs=44218)
              0.03875087 = queryNorm
            0.88782334 = fieldWeight in 1952, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              6.5610886 = idf(docFreq=169, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1952)
      0.5 = coord(1/2)
  0.125 = coord(1/8)

RSWK

Informationssystem / Wissenstechnik / Ontologie <Wissensverarbeitung> / Aufsatzsammlung
Semantic Web / Wissensbasiertes System / Ontologie <Wissensverarbeitung> / Aufsatzsammlung
Informationssystem / Konzeptionelle Modellierung / Ontologie <Wissensverarbeitung> / Aufsatzsammlung

Subject

Informationssystem / Wissenstechnik / Ontologie <Wissensverarbeitung> / Aufsatzsammlung
Semantic Web / Wissensbasiertes System / Ontologie <Wissensverarbeitung> / Aufsatzsammlung
Informationssystem / Konzeptionelle Modellierung / Ontologie <Wissensverarbeitung> / Aufsatzsammlung

0.013477524 = product of:
  0.053910095 = sum of:
    0.040784575 = weight(_text_:work in 3466) [ClassicSimilarity], result of:
      0.040784575 = score(doc=3466,freq=4.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.28674924 = fieldWeight in 3466, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3466)
    0.01312552 = product of:
      0.02625104 = sum of:
        0.02625104 = weight(_text_:22 in 3466) [ClassicSimilarity], result of:
          0.02625104 = score(doc=3466,freq=2.0), product of:
            0.13569894 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03875087 = queryNorm
            0.19345059 = fieldWeight in 3466, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3466)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Specimen identification keys are still the most commonly created tools used by systematic biologists to access biodiversity information. Creating identification keys requires analyzing and synthesizing large amounts of information from specimens and their descriptions and is a very labor-intensive and time-consuming activity. Automating the generation of identification keys from text descriptions becomes a highly attractive text mining application in the biodiversity domain. Fine-grained semantic annotation of morphological descriptions of organisms is a necessary first step in generating keys from text. Machine-readable ontologies are needed in this process because most biological characters are only implied (i.e., not stated) in descriptions. The immediate question to ask is How well do existing ontologies support semantic annotation and automated key generation? With the intention to either select an existing ontology or develop a unified ontology based on existing ones, this paper evaluates the coverage, semantic consistency, and inter-ontology agreement of a biodiversity character ontology and three plant glossaries that may be turned into ontologies. The coverage and semantic consistency of the ontology/glossaries are checked against the authoritative domain literature, namely, Flora of North America and Flora of China. The evaluation results suggest that more work is needed to improve the coverage and interoperability of the ontology/glossaries. More concepts need to be added to the ontology/glossaries and careful work is needed to improve the semantic consistency. The method used in this paper to evaluate the ontology/glossaries can be used to propose new candidate concepts from the domain literature and suggest appropriate definitions.

Date

1. 6.2010 9:55:22

0.013477524 = product of:
  0.053910095 = sum of:
    0.040784575 = weight(_text_:work in 2831) [ClassicSimilarity], result of:
      0.040784575 = score(doc=2831,freq=4.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.28674924 = fieldWeight in 2831, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2831)
    0.01312552 = product of:
      0.02625104 = sum of:
        0.02625104 = weight(_text_:22 in 2831) [ClassicSimilarity], result of:
          0.02625104 = score(doc=2831,freq=2.0), product of:
            0.13569894 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03875087 = queryNorm
            0.19345059 = fieldWeight in 2831, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2831)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

The purpose of this work is to develop an ontology-based framework for developing an information retrieval system to cater to specific queries of users. For creating such an ontology, information was obtained from a wide range of information sources involved with brain tumour study and research. The information thus obtained was compiled and analysed to provide a standard, reliable and relevant information base to aid our proposed system. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as identification of the terminology, analysis, synthesis, standardization and ordering. A vast majority of the ontologies being developed nowadays lack flexibility. This becomes a formidable constraint when it comes to interoperability. We found that a facet-based method provides a distinct guideline for the development of a robust and flexible model concerning the domain of brain tumours. Our attempt has been to bridge library and information science and computer science, which itself involved an experimental approach. It was discovered that a faceted approach is really enduring, as it helps in the achievement of properties like navigation, exploration and faceted browsing. Computer-based brain tumour ontology supports the work of researchers towards gathering information on brain tumour research and allows users across the world to intelligently access new scientific information quickly and efficiently.

Date

12. 3.2016 13:21:22

0.013272952 = product of:
  0.10618362 = sum of:
    0.10618362 = weight(_text_:supported in 826) [ClassicSimilarity], result of:
      0.10618362 = score(doc=826,freq=4.0), product of:
        0.22949564 = queryWeight, product of:
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.03875087 = queryNorm
        0.4626825 = fieldWeight in 826, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.0390625 = fieldNorm(doc=826)
  0.125 = coord(1/8)

Abstract

Several instruments of knowledge organization will reflect different possibilities for information retrieval. In this context, ontologies have a different potential because they allow knowledge discovery, which can be used to retrieve information in a more flexible way. However, this potential can be affected by the theoretical principles adopted in ontology building. The aim of this paper is to discuss, in an introductory way, how a (not exhaustive) set of theoretical principles can influence an aspect of ontologies: their use to obtain inferences. In this context, the role of Ingetraut Dahlberg's Theory of Concept is discussed. The methodology is exploratory, qualitative, and from the technical point of view it uses bibliographic research supported by the content analysis method. It also presents a small example of application as a proof of concept. As results, a discussion about the influence of conceptual definition on subsumption inferences is presented, theoretical contributions are suggested that should be used to guide the formation of hierarchical structures on which such inferences are supported, and examples are provided of how the absence of such contributions can lead to erroneous inferences

0.0131395515 = product of:
  0.10511641 = sum of:
    0.10511641 = weight(_text_:supported in 4644) [ClassicSimilarity], result of:
      0.10511641 = score(doc=4644,freq=2.0), product of:
        0.22949564 = queryWeight, product of:
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.03875087 = queryNorm
        0.45803228 = fieldWeight in 4644, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4644)
  0.125 = coord(1/8)

Abstract

This paper describes a method for converting existing thesauri and related resources from their native format to RDF(S) and OWL. The method identifies four steps in the conversion process. In each step, decisions have to be taken with respect to the syntax or semantics of the resulting representation. Each step is supported through a number of guidelines. The method is illustrated through conversions of two large thesauri: MeSH and WordNet.

0.013004784 = product of:
  0.10403827 = sum of:
    0.10403827 = weight(_text_:supported in 4405) [ClassicSimilarity], result of:
      0.10403827 = score(doc=4405,freq=6.0), product of:
        0.22949564 = queryWeight, product of:
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.03875087 = queryNorm
        0.4533344 = fieldWeight in 4405, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.9223356 = idf(docFreq=321, maxDocs=44218)
          0.03125 = fieldNorm(doc=4405)
  0.125 = coord(1/8)

Abstract

Developing ontologies is central to our vision of Semantic Web-based knowledge management. The methodology described in Chapter 3 guides the development of ontologies for different applications. However, because of the size of ontologies, their complexity, their formal underpinnings and the necessity to come towards a shared understanding within a group of people when defining an ontology, ontology construction is still far from being a well-understood process. Concerning the methodology, OntoEdit focuses on three of the main steps for ontology development (the methodology is described in Chapter 3), viz. the kick off, refinement, and evaluation. We describe the steps supported by OntoEdit and focus on collaborative aspects that occur during each of the step. First, all requirements of the envisaged ontology are collected during the kick off phase. Typically for ontology engineering, ontology engineers and domain experts are joined in a team that works together on a description of the domain and the goal of the ontology, design guidelines, available knowledge sources (e.g. re-usable ontologies and thesauri, etc.), potential users and use cases and applications supported by the ontology. The output of this phase is a semiformal description of the ontology. Second, during the refinement phase, the team extends the semi-formal description in several iterations and formalizes it in an appropriate representation language like RDF(S) or, more advanced, DAML1OIL. The output of this phase is a mature ontology (the 'target ontology'). Third, the target ontology needs to be evaluated according to the requirement specifications. Typically this phase serves as a proof for the usefulness of ontologies (and ontology-based applications) and may involve the engineering team as well as end users of the targeted application. The output of this phase is an evaluated ontology, ready for roll-out into a productive environment. Support for these collaborative development steps within the ontology development methodology is crucial in order to meet the conflicting needs for ease of use and construction of complex ontology structures. We now illustrate OntoEdit's support for each of the supported steps. The examples shown are taken from the Swiss Life case study on skills management (cf. Chapter 12).

0.012589371 = product of:
  0.050357483 = sum of:
    0.034606863 = weight(_text_:work in 2623) [ClassicSimilarity], result of:
      0.034606863 = score(doc=2623,freq=2.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.2433148 = fieldWeight in 2623, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.046875 = fieldNorm(doc=2623)
    0.015750622 = product of:
      0.031501245 = sum of:
        0.031501245 = weight(_text_:22 in 2623) [ClassicSimilarity], result of:
          0.031501245 = score(doc=2623,freq=2.0), product of:
            0.13569894 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03875087 = queryNorm
            0.23214069 = fieldWeight in 2623, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2623)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Contemporary retrieval systems, which search across collections, usually ignore collection-level metadata. Alternative approaches, exploiting collection-level information, will require an understanding of the various kinds of relationships that can obtain between collection-level and item-level metadata. This paper outlines the problem and describes a project that is developing a logic-based framework for classifying collection/item metadata relationships. This framework will support (i) metadata specification developers defining metadata elements, (ii) metadata creators describing objects, and (iii) system designers implementing systems that take advantage of collection-level metadata. We present three examples of collection/item metadata relationship categories, attribute/value-propagation, value-propagation, and value-constraint and show that even in these simple cases a precise formulation requires modal notions in addition to first-order logic. These formulations are related to recent work in information retrieval and ontology evaluation.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

0.012589371 = product of:
  0.050357483 = sum of:
    0.034606863 = weight(_text_:work in 3387) [ClassicSimilarity], result of:
      0.034606863 = score(doc=3387,freq=2.0), product of:
        0.14223081 = queryWeight, product of:
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.03875087 = queryNorm
        0.2433148 = fieldWeight in 3387, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6703904 = idf(docFreq=3060, maxDocs=44218)
          0.046875 = fieldNorm(doc=3387)
    0.015750622 = product of:
      0.031501245 = sum of:
        0.031501245 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
          0.031501245 = score(doc=3387,freq=2.0), product of:
            0.13569894 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03875087 = queryNorm
            0.23214069 = fieldWeight in 3387, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3387)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.

Date

1. 8.2010 12:35:22