Search (31 results, page 1 of 2)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  1. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.01 
    0.006014771 = product of:
  0.024059083 = sum of:
    0.014315128 = product of:
      0.042945385 = sum of:
        0.042945385 = weight(_text_:problem in 1852) [ClassicSimilarity], result of:
          0.042945385 = score(doc=1852,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.3282676 = fieldWeight in 1852, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1852)
      0.33333334 = coord(1/3)
    0.009743956 = product of:
      0.029231867 = sum of:
        0.029231867 = weight(_text_:22 in 1852) [ClassicSimilarity], result of:
          0.029231867 = score(doc=1852,freq=2.0), product of:
            0.10793405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.030822188 = 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)
      0.33333334 = coord(1/3)
  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
  2. Jun, W.: ¬A knowledge network constructed by integrating classification, thesaurus and metadata in a digital library (2003) 0.00 
    0.0034496475 = product of:
  0.01379859 = sum of:
    0.008180073 = product of:
      0.02454022 = sum of:
        0.02454022 = weight(_text_:problem in 1254) [ClassicSimilarity], result of:
          0.02454022 = score(doc=1254,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.1875815 = fieldWeight in 1254, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03125 = fieldNorm(doc=1254)
      0.33333334 = coord(1/3)
    0.0056185164 = product of:
      0.016855549 = sum of:
        0.016855549 = weight(_text_:29 in 1254) [ClassicSimilarity], result of:
          0.016855549 = score(doc=1254,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.15546128 = fieldWeight in 1254, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.03125 = fieldNorm(doc=1254)
      0.33333334 = coord(1/3)
  0.25 = coord(2/8)
    Abstract
    Knowledge management in digital libraries is a universal problem. Keyword-based searching is applied everywhere no matter whether the resources are indexed databases or full-text Web pages. In keyword matching, the valuable content description and indexing of the metadata, such as the subject descriptors and the classification notations, are merely treated as common keywords to be matched with the user query. Without the support of vocabulary control tools, such as classification systems and thesauri, the intelligent labor of content analysis, description and indexing in metadata production are seriously wasted. New retrieval paradigms are needed to exploit the potential of the metadata resources. Could classification and thesauri, which contain the condensed intelligence of generations of librarians, be used in a digital library to organize the networked information, especially metadata, to facilitate their usability and change the digital library into a knowledge management environment? To examine that question, we designed and implemented a new paradigm that incorporates a classification system, a thesaurus and metadata. The classification and the thesaurus are merged into a concept network, and the metadata are distributed into the nodes of the concept network according to their subjects. The abstract concept node instantiated with the related metadata records becomes a knowledge node. A coherent and consistent knowledge network is thus formed. It is not only a framework for resource organization but also a structure for knowledge navigation, retrieval and learning. We have built an experimental system based on the Chinese Classification and Thesaurus, which is the most comprehensive and authoritative in China, and we have incorporated more than 5000 bibliographic records in the computing domain from the Peking University Library. The result is encouraging. In this article, we review the tools, the architecture and the implementation of our experimental system, which is called Vision.
    Source
    Bulletin of the American Society for Information Science. 29(2003) no.2, S.24-28
  3. Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006) 0.00 
    0.003437012 = product of:
  0.013748048 = sum of:
    0.008180073 = product of:
      0.02454022 = sum of:
        0.02454022 = weight(_text_:problem in 1163) [ClassicSimilarity], result of:
          0.02454022 = score(doc=1163,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.1875815 = fieldWeight in 1163, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03125 = fieldNorm(doc=1163)
      0.33333334 = coord(1/3)
    0.005567975 = product of:
      0.016703924 = sum of:
        0.016703924 = weight(_text_:22 in 1163) [ClassicSimilarity], result of:
          0.016703924 = score(doc=1163,freq=2.0), product of:
            0.10793405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.030822188 = queryNorm
            0.15476047 = fieldWeight in 1163, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=1163)
      0.33333334 = coord(1/3)
  0.25 = coord(2/8)
    Abstract
    This paper addresses the problem of information discovery in large collections of text. For users, one of the key problems in working with such collections is determining where to focus their attention. In selecting documents for examination, users must be able to formulate reasonably precise queries. Queries that are too broad will greatly reduce the efficiency of information discovery efforts by overwhelming the users with peripheral information. In order to formulate efficient queries, a mechanism is needed to automatically alert users regarding potentially interesting information contained within the collection. This paper presents the results of an experiment designed to test one approach to generation of such alerts. The technique of latent semantic indexing (LSI) is used to identify relationships among entities of interest. Entity extraction software is used to pre-process the text of the collection so that the LSI space contains representation vectors for named entities in addition to those for individual terms. In the LSI space, the cosine of the angle between the representation vectors for two entities captures important information regarding the degree of association of those two entities. For appropriate choices of entities, determining the entity pairs with the highest mutual cosine values yields valuable information regarding the contents of the text collection. The test database used for the experiment consists of 150,000 news articles. The proposed approach for alert generation is tested using a counterterrorism analysis example. The approach is shown to have significant potential for aiding users in rapidly focusing on information of potential importance in large text collections. The approach also has value in identifying possible use of aliases.
    Source
    Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]
  4. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.00 
    0.0024607205 = product of:
  0.019685764 = sum of:
    0.019685764 = product of:
      0.059057288 = sum of:
        0.059057288 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
          0.059057288 = score(doc=1352,freq=4.0), product of:
            0.10793405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.030822188 = queryNorm
            0.54716086 = fieldWeight in 1352, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=1352)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
  5. Ross, J.: ¬A new way of information retrieval : 3-D indexing and concept mapping (2000) 0.00 
    0.0021069439 = product of:
  0.01685555 = sum of:
    0.01685555 = product of:
      0.05056665 = sum of:
        0.05056665 = weight(_text_:29 in 6171) [ClassicSimilarity], result of:
          0.05056665 = score(doc=6171,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.46638384 = fieldWeight in 6171, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.09375 = fieldNorm(doc=6171)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    25. 2.1997 10:29:16
  6. Shiri, A.A.; Revie, C.; Chowdhury, G.: Thesaurus-enhanced search interfaces (2002) 0.00 
    0.0021069439 = product of:
  0.01685555 = sum of:
    0.01685555 = product of:
      0.05056665 = sum of:
        0.05056665 = weight(_text_:29 in 3807) [ClassicSimilarity], result of:
          0.05056665 = score(doc=3807,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.46638384 = fieldWeight in 3807, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.09375 = fieldNorm(doc=3807)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    18. 5.2002 17:29:00
  7. Shiri, A.A.; Revie, C.: ¬The effects of topic complexity and familiarity on cognitive and physical moves in a thesaurus-enhanced search environment (2003) 0.00 
    0.0021069439 = product of:
  0.01685555 = sum of:
    0.01685555 = product of:
      0.05056665 = sum of:
        0.05056665 = weight(_text_:29 in 4695) [ClassicSimilarity], result of:
          0.05056665 = score(doc=4695,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.46638384 = fieldWeight in 4695, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.09375 = fieldNorm(doc=4695)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Source
    Journal of information science. 29(2003) no.6, S.517-
  8. Stojanovic, N.: On the query refinement in the ontology-based searching for information (2005) 0.00 
    0.0017557865 = product of:
  0.014046292 = sum of:
    0.014046292 = product of:
      0.042138875 = sum of:
        0.042138875 = weight(_text_:29 in 2907) [ClassicSimilarity], result of:
          0.042138875 = score(doc=2907,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.38865322 = fieldWeight in 2907, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.078125 = fieldNorm(doc=2907)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    5. 4.1996 15:29:15
  9. Sacco, G.M.: Dynamic taxonomies and guided searches (2006) 0.00 
    0.0017225044 = product of:
  0.013780035 = sum of:
    0.013780035 = product of:
      0.041340105 = sum of:
        0.041340105 = weight(_text_:22 in 5295) [ClassicSimilarity], result of:
          0.041340105 = score(doc=5295,freq=4.0), product of:
            0.10793405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.030822188 = queryNorm
            0.38301262 = fieldWeight in 5295, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5295)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    22. 7.2006 17:56:22
  10. Hovy, E.: Comparing sets of semantic relations in ontologies (2002) 0.00 
    0.0015337638 = product of:
  0.012270111 = sum of:
    0.012270111 = product of:
      0.03681033 = sum of:
        0.03681033 = weight(_text_:problem in 2178) [ClassicSimilarity], result of:
          0.03681033 = score(doc=2178,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.28137225 = fieldWeight in 2178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=2178)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    A set of semantic relations is created every time a domain modeler wants to solve some complex problem computationally. These relations are usually organized into ontologies. But three is little standardization of ontologies today, and almost no discussion an ways of comparing relations, of determining a general approach to creating relations, or of modeling in general. This chapter outlines an approach to establishing a general methodology for comparing and justifying sets of relations (and ontologies in general). It first provides several dozen characteristics of ontologies, organized into three taxonomies of increasingly detailed features, by which many essential characteristics of ontologies can be described. These features enable one to compare ontologies at a general level, without studying every concept they contain. But sometimes it is necessary to make detailed comparisons of content. The chapter then illustrates one method for determining salient points for comparison, using algorithms that semi-automatically identify similarities and differences between ontologies.
  11. Zazo, A.F.; Figuerola, C.G.; Berrocal, J.L.A.; Rodriguez, E.: Reformulation of queries using similarity-thesauri (2005) 0.00 
    0.0015337638 = product of:
  0.012270111 = sum of:
    0.012270111 = product of:
      0.03681033 = sum of:
        0.03681033 = weight(_text_:problem in 1043) [ClassicSimilarity], result of:
          0.03681033 = score(doc=1043,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.28137225 = fieldWeight in 1043, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=1043)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    One of the major problems in information retrieval is the formulation of queries on the part of the user. This entails specifying a set of words or terms that express their informational need. However, it is well-known that two people can assign different terms to refer to the same concepts. The techniques that attempt to reduce this problem as much as possible generally start from a first search, and then study how the initial query can be modified to obtain better results. In general, the construction of the new query involves expanding the terms of the initial query and recalculating the importance of each term in the expanded query. Depending on the technique used to formulate the new query several strategies are distinguished. These strategies are based on the idea that if two terms are similar (with respect to any criterion), the documents in which both terms appear frequently will also be related. The technique we used in this study is known as query expansion using similarity thesauri.
  12. Sacco, G.M.: Accessing multimedia infobases through dynamic taxonomies (2004) 0.00 
    0.0014046291 = product of:
  0.011237033 = sum of:
    0.011237033 = product of:
      0.033711098 = sum of:
        0.033711098 = weight(_text_:29 in 2637) [ClassicSimilarity], result of:
          0.033711098 = score(doc=2637,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.31092256 = fieldWeight in 2637, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0625 = fieldNorm(doc=2637)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    29. 8.2004 10:15:02
  13. Tudhope, D.; Binding, C.; Blocks, D.; Cunliffe, D.: Compound descriptors in context : a matching function for classifications and thesauri (2002) 0.00 
    0.0012781365 = product of:
  0.010225092 = sum of:
    0.010225092 = product of:
      0.030675275 = sum of:
        0.030675275 = weight(_text_:problem in 3179) [ClassicSimilarity], result of:
          0.030675275 = score(doc=3179,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.23447686 = fieldWeight in 3179, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3179)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    There are many advantages for Digital Libraries in indexing with classifications or thesauri, but some current disincentive in the lack of flexible retrieval tools that deal with compound descriptors. This paper discusses a matching function for compound descriptors, or multi-concept subject headings, that does not rely an exact matching but incorporates term expansion via thesaurus semantic relationships to produce ranked results that take account of missing and partially matching terms. The matching function is based an a measure of semantic closeness between terms, which has the potential to help with recall problems. The work reported is part of the ongoing FACET project in collaboration with the National Museum of Science and Industry and its collections database. The architecture of the prototype system and its Interface are outlined. The matching problem for compound descriptors is reviewed and the FACET implementation described. Results are discussed from scenarios using the faceted Getty Art and Architecture Thesaurus. We argue that automatic traversal of thesaurus relationships can augment the user's browsing possibilities. The techniques can be applied both to unstructured multi-concept subject headings and potentially to more syntactically structured strings. The notion of a focus term is used by the matching function to model AAT modified descriptors (noun phrases). The relevance of the approach to precoordinated indexing and matching faceted strings is discussed.
  14. Lehtokangas, R.; Järvelin, K.: Consistency of textual expression in newspaper articles : an argument for semantically based query expansion (2001) 0.00 
    0.0012781365 = product of:
  0.010225092 = sum of:
    0.010225092 = product of:
      0.030675275 = sum of:
        0.030675275 = weight(_text_:problem in 4485) [ClassicSimilarity], result of:
          0.030675275 = score(doc=4485,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.23447686 = fieldWeight in 4485, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4485)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    This article investigates how consistent different newspapers are in their choice of words when writing about the same news events. News articles on the same news events were taken from three Finnish newspapers and compared in regard to their central concepts and words representing the concepts in the news texts. Consistency figures were calculated for each set of three articles (the total number of sets was sixty). Inconsistency in words and concepts was found between news articles from different newspapers. The mean value of consistency calculated on the basis of words was 65 per cent; this however depended on the article length. For short news wires consistency was 83 per cent while for long articles it was only 47 per cent. At the concept level, consistency was considerably higher, ranging from 92 per cent to 97 per cent between short and long articles. The articles also represented three categories of topic (event, process and opinion). Statistically significant differences in consistency were found in regard to length but not in regard to the categories of topic. We argue that the expression inconsistency is a clear sign of a retrieval problem and that query expansion based on semantic relationships can significantly improve retrieval performance on free-text sources.
  15. Niemi, T.; Jämsen , J.: ¬A query language for discovering semantic associations, part I : approach and formal definition of query primitives (2007) 0.00 
    0.0012781365 = product of:
  0.010225092 = sum of:
    0.010225092 = product of:
      0.030675275 = sum of:
        0.030675275 = weight(_text_:problem in 591) [ClassicSimilarity], result of:
          0.030675275 = score(doc=591,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.23447686 = fieldWeight in 591, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=591)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    In contemporary query languages, the user is responsible for navigation among semantically related data. Because of the huge amount of data and the complex structural relationships among data in modern applications, it is unrealistic to suppose that the user could know completely the content and structure of the available information. There are several query languages whose purpose is to facilitate navigation in unknown structures of databases. However, the background assumption of these languages is that the user knows how data are related to each other semantically in the structure at hand. So far only little attention has been paid to how unknown semantic associations among available data can be discovered. We address this problem in this article. A semantic association between two entities can be constructed if a sequence of relationships expressed explicitly in a database can be found that connects these entities to each other. This sequence may contain several other entities through which the original entities are connected to each other indirectly. We introduce an expressive and declarative query language for discovering semantic associations. Our query language is able, for example, to discover semantic associations between entities for which only some of the characteristics are known. Further, it integrates the manipulation of semantic associations with the manipulation of documents that may contain information on entities in semantic associations.
  16. Tudhope, D.; Alani, H.; Jones, C.: Augmenting thesaurus relationships : possibilities for retrieval (2001) 0.00 
    0.0012781365 = product of:
  0.010225092 = sum of:
    0.010225092 = product of:
      0.030675275 = sum of:
        0.030675275 = weight(_text_:problem in 1520) [ClassicSimilarity], result of:
          0.030675275 = score(doc=1520,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.23447686 = fieldWeight in 1520, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1520)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    This paper discusses issues concerning the augmentation of thesaurus relationships, in light of new application possibilities for retrieval. We first discuss a case study that explored the retrieval potential of an augmented set of thesaurus relationships by specialising standard relationships into richer subtypes, in particular hierarchical geographical containment and the associative relationship. We then locate this work in a broader context by reviewing various attempts to build taxonomies of thesaurus relationships, and conclude by discussing the feasibility of hierarchically augmenting the core set of thesaurus relationships, particularly the associative relationship. We discuss the possibility of enriching the specification and semantics of Related Term (RT relationships), while maintaining compatibility with traditional thesauri via a limited hierarchical extension of the associative (and hierarchical) relationships. This would be facilitated by distinguishing the type of term from the (sub)type of relationship and explicitly specifying semantic categories for terms following a faceted approach. We first illustrate how hierarchical spatial relationships can be used to provide more flexible retrieval for queries incorporating place names in applications employing online gazetteers and geographical thesauri. We then employ a set of experimental scenarios to investigate key issues affecting use of the associative (RT) thesaurus relationships in semantic distance measures. Previous work has noted the potential of RTs in thesaurus search aids but also the problem of uncontrolled expansion of query term sets. Results presented in this paper suggest the potential for taking account of the hierarchical context of an RT link and specialisations of the RT relationship
  17. Frederichs, A.: Natürlichsprachige Abfrage und 3-D-Visualisierung von Wissenszusammenhängen (2007) 0.00 
    0.0012415285 = product of:
  0.009932228 = sum of:
    0.009932228 = product of:
      0.029796684 = sum of:
        0.029796684 = weight(_text_:29 in 566) [ClassicSimilarity], result of:
          0.029796684 = score(doc=566,freq=4.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.2748193 = fieldWeight in 566, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=566)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    25.10.2007 9:51:29
    Source
    Wa(h)re Information: 29. Österreichischer Bibliothekartag Bregenz, 19.-23.9.2006. Hrsg.: Harald Weigel
  18. Faaborg, A.; Lagoze, C.: Semantic browsing (2003) 0.00 
    0.0012179945 = product of:
  0.009743956 = sum of:
    0.009743956 = product of:
      0.029231867 = sum of:
        0.029231867 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
          0.029231867 = score(doc=1026,freq=2.0), product of:
            0.10793405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.030822188 = queryNorm
            0.2708308 = fieldWeight in 1026, 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=1026)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Source
    Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003
  19. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.00 
    0.0011068988 = product of:
  0.00885519 = sum of:
    0.00885519 = product of:
      0.02656557 = sum of:
        0.02656557 = weight(_text_:problem in 1211) [ClassicSimilarity], result of:
          0.02656557 = score(doc=1211,freq=6.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.20306295 = fieldWeight in 1211, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
    From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
  20. Greenberg, J.: Optimal query expansion (QE) processing methods with semantically encoded structured thesaurus terminology (2001) 0.00 
    0.0010534719 = product of:
  0.008427775 = sum of:
    0.008427775 = product of:
      0.025283325 = sum of:
        0.025283325 = weight(_text_:29 in 5750) [ClassicSimilarity], result of:
          0.025283325 = score(doc=5750,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.23319192 = fieldWeight in 5750, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.046875 = fieldNorm(doc=5750)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    29. 9.2001 14:00:11

Authors

Languages

  • e 28
  • d 3

Themes