Search (22 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × year_i:[2000 TO 2010}
  1. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.02
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
  2. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.02
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    Date
    22. 3.2003 19:35:46
  3. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.02
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    Abstract
    The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.
    Date
    22. 7.2006 16:32:43
  4. Sacco, G.M.: Dynamic taxonomies and guided searches (2006) 0.02
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    Date
    22. 7.2006 17:56:22
  5. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.01
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    Date
    16.11.2008 16:22:48
  6. Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006) 0.01
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    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]
  7. Ross, J.: ¬A new way of information retrieval : 3-D indexing and concept mapping (2000) 0.01
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  8. Blocks, D.; Cunliffe, D.; Tudhope, D.: ¬A reference model for user-system interaction in thesaurus-based searching (2006) 0.00
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  9. Tudhope, D.; Blocks, D.; Cunliffe, D.; Binding, C.: Query expansion via conceptual distance in thesaurus indexed collections (2006) 0.00
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  10. Tudhope, D.; Binding, C.; Blocks, D.; Cunliffe, D.: Compound descriptors in context : a matching function for classifications and thesauri (2002) 0.00
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  11. Tudhope, D.; Binding, C.; Blocks, D.; Cunliffe, D.: FACET: thesaurus retrieval with semantic term expansion (2002) 0.00
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  12. Sanderson, M.; Lawrie, D.: Building, testing, and applying concept hierarchies (2000) 0.00
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  13. Vallet, D.; Fernández, M.; Castells, P.: ¬An ontology-based information retrieval model (2005) 0.00
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  14. Yan, X.; Li, X.; Song, D.: ¬A correlation analysis on LSA and HAL semantic space models (2004) 0.00
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  15. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.00
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    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.
    Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.
    Content
    The JAVA applet is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. A prototype of this interface has been developed and is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. The D-Lib search interface is available at <http://www.dlib.org/Architext/AT-dlib2query.html>.
    Source
    D-Lib magazine. 8(2002) no.10, x S
  16. Wolfram, D.; Xie, H.I.: Traditional IR for web users : a context for general audience digital libraries (2002) 0.00
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  17. Kelly, D.: Measuring online information seeking context : Part 1: background and method (2006) 0.00
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  18. Kelly, D.: Measuring online information seeking context : Part 2: Findings and discussion (2006) 0.00
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  19. Li, D.; Kwong, C.-P.; Lee, D.L.: Unified linear subspace approach to semantic analysis (2009) 0.00
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  20. Tudhope, D.; Alani, H.; Jones, C.: Augmenting thesaurus relationships : possibilities for retrieval (2001) 0.00
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