Search (233 results, page 1 of 12)

0.100927494 = product of:
  0.16821249 = sum of:
    0.050497327 = weight(_text_:list in 1800) [ClassicSimilarity], result of:
      0.050497327 = score(doc=1800,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.20045151 = fieldWeight in 1800, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1800)
    0.021554895 = weight(_text_:of in 1800) [ClassicSimilarity], result of:
      0.021554895 = score(doc=1800,freq=44.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.28363106 = fieldWeight in 1800, product of:
          6.6332498 = tf(freq=44.0), with freq of:
            44.0 = termFreq=44.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1800)
    0.09616026 = weight(_text_:subject in 1800) [ClassicSimilarity], result of:
      0.09616026 = score(doc=1800,freq=32.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.5532265 = fieldWeight in 1800, product of:
          5.656854 = tf(freq=32.0), with freq of:
            32.0 = termFreq=32.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1800)
  0.6 = coord(3/5)

Abstract

The SAC Subcommittee on Subject Relationships/Reference Structures was authorized at the 1995 Midwinter Meeting and appointed shortly before Annual Conference. Its creation was one result of a discussion of how (and why) to promote the display and use of broader-term subject heading references, and its charge reads as follows: To investigate: (1) the kinds of relationships that exist between subjects, the display of which are likely to be useful to catalog users; (2) how these relationships are or could be recorded in authorities and classification formats; (3) options for how these relationships should be presented to users of online and print catalogs, indexes, lists, etc. By the summer 1996 Annual Conference, make some recommendations to SAC about how to disseminate the information and/or implement changes. At that time assess the need for additional time to investigate these issues. The Subcommittee's work on each of the imperatives in the charge was summarized in a report issued at the 1996 Annual Conference (Appendix A). Highlights of this work included the development of a taxonomy of 165 subject relationships; a demonstration that, using existing MARC coding, catalog systems could be programmed to generate references they do not currently support; and an examination of reference displays in several CD-ROM database products. Since that time, work has continued on identifying term relationships and display options; on tracking research, discussion, and implementation of subject relationships in information systems; and on compiling a list of further research needs.

Content

Enthält: Appendix A: Subcommittee on Subject Relationships/Reference Structures - REPORT TO THE ALCTS/CCS SUBJECT ANALYSIS COMMITTEE - July 1996 Appendix B (part 1): Taxonomy of Subject Relationships. Compiled by Dee Michel with the assistance of Pat Kuhr - June 1996 draft (alphabetical display) (Separat in: http://web2.ala.org/ala/alctscontent/CCS/committees/subjectanalysis/subjectrelations/msrscu2.pdf) Appendix B (part 2): Taxonomy of Subject Relationships. Compiled by Dee Michel with the assistance of Pat Kuhr - June 1996 draft (hierarchical display) Appendix C: Checklist of Candidate Subject Relationships for Information Retrieval. Compiled by Dee Michel, Pat Kuhr, and Jane Greenberg; edited by Greg Wool - June 1997 Appendix D: Review of Reference Displays in Selected CD-ROM Abstracts and Indexes by Harriette Hemmasi and Steven Riel Appendix E: Analysis of Relationships in Six LC Subject Authority Records by Harriette Hemmasi and Gary Strawn Appendix F: Report of a Preliminary Survey of Subject Referencing in OPACs by Gregory Wool Appendix G: LC Subject Referencing in OPACs--Why Bother? by Gregory Wool Appendix H: Research Needs on Subject Relationships and Reference Structures in Information Access compiled by Jane Greenberg and Steven Riel with contributions from Dee Michel and others edited by Gregory Wool Appendix I: Bibliography on Subject Relationships compiled mostly by Dee Michel with additional contributions from Jane Greenberg, Steven Riel, and Gregory Wool

0.06930826 = product of:
  0.11551376 = sum of:
    0.009191038 = weight(_text_:of in 1465) [ClassicSimilarity], result of:
      0.009191038 = score(doc=1465,freq=2.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.120940685 = fieldWeight in 1465, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1465)
    0.08327723 = weight(_text_:subject in 1465) [ClassicSimilarity], result of:
      0.08327723 = score(doc=1465,freq=6.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.4791082 = fieldWeight in 1465, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1465)
    0.023045486 = product of:
      0.04609097 = sum of:
        0.04609097 = weight(_text_:22 in 1465) [ClassicSimilarity], result of:
          0.04609097 = score(doc=1465,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = queryNorm
            0.2708308 = fieldWeight in 1465, 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=1465)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

"Explore" is a user task introduced in the Functional Requirements for Subject Authority Data (FRSAD) final report. Through various case scenarios, the authors discuss how structured data, presented based on Linked Data principles and using knowledge organisation systems (KOS) as the backbone, extend the explore task within and beyond subject authority data.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.06838938 = product of:
  0.11398229 = sum of:
    0.020760437 = weight(_text_:of in 3179) [ClassicSimilarity], result of:
      0.020760437 = score(doc=3179,freq=20.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.27317715 = fieldWeight in 3179, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3179)
    0.048568267 = weight(_text_:subject in 3179) [ClassicSimilarity], result of:
      0.048568267 = score(doc=3179,freq=4.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.27942157 = fieldWeight in 3179, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3179)
    0.044653583 = product of:
      0.08930717 = sum of:
        0.08930717 = weight(_text_:headings in 3179) [ClassicSimilarity], result of:
          0.08930717 = score(doc=3179,freq=4.0), product of:
            0.23569997 = queryWeight, product of:
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.04859849 = queryNorm
            0.3789019 = fieldWeight in 3179, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3179)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

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.

Source

Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries : JCDL 2002 ; July 14 - 18, 2002, Portland, Oregon, USA. Ed. by Gary Marchionini

0.06799054 = product of:
  0.113317564 = sum of:
    0.011141219 = weight(_text_:of in 1464) [ClassicSimilarity], result of:
      0.011141219 = score(doc=1464,freq=4.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.14660224 = fieldWeight in 1464, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1464)
    0.082423076 = weight(_text_:subject in 1464) [ClassicSimilarity], result of:
      0.082423076 = score(doc=1464,freq=8.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.4741941 = fieldWeight in 1464, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=1464)
    0.019753272 = product of:
      0.039506543 = sum of:
        0.039506543 = weight(_text_:22 in 1464) [ClassicSimilarity], result of:
          0.039506543 = score(doc=1464,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = queryNorm
            0.23214069 = fieldWeight in 1464, 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=1464)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

This paper attempts to explore an approach of using an automatic semantic analysis tool to enhance the "subject" access to materials that are not included in the usual library subject cataloging process. Using two research samples the authors analyzed the access points supplied by OpenCalais, a semantic analysis tool. As an aid in understanding how computerized subject analysis might be approached, this paper suggests using the three-layer framework that has been accepted and applied in image analysis, developed by Erwin Panofsky.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.06680522 = product of:
  0.11134203 = sum of:
    0.07213905 = weight(_text_:list in 5697) [ClassicSimilarity], result of:
      0.07213905 = score(doc=5697,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.2863593 = fieldWeight in 5697, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5697)
    0.022741921 = weight(_text_:of in 5697) [ClassicSimilarity], result of:
      0.022741921 = score(doc=5697,freq=24.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.2992506 = fieldWeight in 5697, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5697)
    0.016461061 = product of:
      0.032922123 = sum of:
        0.032922123 = weight(_text_:22 in 5697) [ClassicSimilarity], result of:
          0.032922123 = score(doc=5697,freq=2.0), product of:
            0.17018363 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04859849 = queryNorm
            0.19345059 = fieldWeight in 5697, 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=5697)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

The performance of 8 ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. Focuses on the identification of algorithms that most effectively take cognizance of user preferences. user choices (i.e. the terms selected by the searchers for the query expansion search) provided the yardstick for the evaluation of the 8 ranking algorithms. This methodology introduces a user oriented approach in evaluating ranking algorithms for query expansion in contrast to the standard, system oriented approaches. Similarities in the performance of the 8 algorithms and the ways these algorithms rank terms were the main focus of this evaluation. The findings demonstrate that the r-lohi, wpq, enim, and porter algorithms have similar performance in bringing good terms to the top of a ranked list of terms for query expansion. However, further evaluation of the algorithms in different (e.g. full text) environments is needed before these results can be generalized beyond the context of the present study

Date

22. 2.1996 13:14:10

0.06402767 = product of:
  0.10671277 = sum of:
    0.016569378 = weight(_text_:of in 2768) [ClassicSimilarity], result of:
      0.016569378 = score(doc=2768,freq=26.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.21802892 = fieldWeight in 2768, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=2768)
    0.058885895 = weight(_text_:subject in 2768) [ClassicSimilarity], result of:
      0.058885895 = score(doc=2768,freq=12.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.33878064 = fieldWeight in 2768, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.02734375 = fieldNorm(doc=2768)
    0.031257506 = product of:
      0.06251501 = sum of:
        0.06251501 = weight(_text_:headings in 2768) [ClassicSimilarity], result of:
          0.06251501 = score(doc=2768,freq=4.0), product of:
            0.23569997 = queryWeight, product of:
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.04859849 = queryNorm
            0.2652313 = fieldWeight in 2768, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.02734375 = fieldNorm(doc=2768)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Subject searching is the most common but also the most conflictive searching for end user. The aim of this paper is to check how users expressions match subject headings and to prove if knowledge structure used in online catalogs enhances searching effectiveness. A bibliographic revision about difficulties in subject access and proposed methods to improve it is also presented. For the empirical analysis, transaction logs from two university libraries, online catalogs (CISNE and FAMA) were collected. Results show that more than a quarter of user queries are effective due to an alphabetical subject index approach and browsing through hypertextual links. 1. Introduction Since the 1980's, online public access catalogs (OPAC's) have become usual way to access bibliographic information. During the last two decades the technological development has helped to extend their use, making feasible the access for a whole of users that is getting more and more extensive and heterogeneous, and also to incorporate information resources in electronic formats and to interconnect systems. However, technology seems to have developed faster than our knowledge about the tasks where it has been applied and than the evolution of our capacities for adapting to it. The conceptual model of OPAC has been hardly modified recently, and for interacting with them, users still need to combine the same skills and basic knowledge than at the beginning of its introduction (Borgman, 1986, 2000): a) conceptual knowledge to translate the information need into an appropriate query because of a well-designed mental model of the system, b) semantic and syntactic knowledge to be able to implement that query (access fields, searching type, Boolean logic, etc.) and c) basic technical skills in computing. At present many users have the essential technical skills to make use, with more or less expertise, of a computer. This number is substantially reduced when it is referred to the conceptual, semantic and syntactic knowledge that is necessary to achieve a moderately satisfactory search. An added difficulty arises in subject searching, as users should concrete their unknown information needs in terms that the information retrieval system can understand. Many researches have focused an unskilled searchers' difficulties to enter an effective query. The mental models influence, users assumption about characteristics, structure, contents and operation of the system they interact with have been analysed (Dillon, 2000; Dimitroff, 2000). Another issue that implies difficulties is vocabulary: how to find the right terms to implement a query and to modify it as the case may be. Terminology and expressions characteristics used in searching (Bates, 1993), the match between user terms and the subject headings from the catalog (Carlyle, 1989; Drabensttot, 1996; Drabensttot & Vizine-Goetz, 1994), the incidence of spelling errors (Drabensttot and Weller, 1996; Ferl and Millsap, 1996; Walker and Jones, 1987), users problems

Source

Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas

0.05849369 = product of:
  0.097489476 = sum of:
    0.051010005 = weight(_text_:list in 1211) [ClassicSimilarity], result of:
      0.051010005 = score(doc=1211,freq=4.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.2024866 = fieldWeight in 1211, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
    0.016737599 = weight(_text_:of in 1211) [ClassicSimilarity], result of:
      0.016737599 = score(doc=1211,freq=52.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.22024246 = fieldWeight in 1211, product of:
          7.2111025 = tf(freq=52.0), with freq of:
            52.0 = termFreq=52.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
    0.029741868 = weight(_text_:subject in 1211) [ClassicSimilarity], result of:
      0.029741868 = score(doc=1211,freq=6.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.17111006 = fieldWeight in 1211, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
  0.6 = coord(3/5)

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.
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>.

0.058423247 = product of:
  0.14605811 = sum of:
    0.12242402 = weight(_text_:list in 2733) [ClassicSimilarity], result of:
      0.12242402 = score(doc=2733,freq=4.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.48596787 = fieldWeight in 2733, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
    0.023634095 = weight(_text_:of in 2733) [ClassicSimilarity], result of:
      0.023634095 = score(doc=2733,freq=18.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.3109903 = fieldWeight in 2733, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
  0.4 = coord(2/5)

Abstract

We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities.

0.048618577 = product of:
  0.12154644 = sum of:
    0.100994654 = weight(_text_:list in 141) [ClassicSimilarity], result of:
      0.100994654 = score(doc=141,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.40090302 = fieldWeight in 141, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.0546875 = fieldNorm(doc=141)
    0.020551786 = weight(_text_:of in 141) [ClassicSimilarity], result of:
      0.020551786 = score(doc=141,freq=10.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.2704316 = fieldWeight in 141, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=141)
  0.4 = coord(2/5)

Abstract

This paper reports on an evaluation of the browsing behaviour of end users of a WWW-OPAC focussing on the browsing relevance feedback (BRF) strategy. Results of this study reveal that BRF is a popular strategy. We also find that the relationships involved in the BRF strategy are generally syntagmatic

Source

Structures and relations in knowledge organization: Proceedings of the 5th International ISKO-Conference, Lille, 25.-29.8.1998. Ed.: W. Mustafa el Hadi et al

0.0459886 = product of:
  0.1149715 = sum of:
    0.08656685 = weight(_text_:list in 37) [ClassicSimilarity], result of:
      0.08656685 = score(doc=37,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.34363115 = fieldWeight in 37, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.046875 = fieldNorm(doc=37)
    0.02840465 = weight(_text_:of in 37) [ClassicSimilarity], result of:
      0.02840465 = score(doc=37,freq=26.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.37376386 = fieldWeight in 37, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=37)
  0.4 = coord(2/5)

Abstract

A means of automatically deriving a hierarchical organization of concepts from a set of documents without use of training data or standard clustering techniques is presented. Using a process that extracts salient words and phrases from the documents, these terms are organized hierarchically using a type of co-occurrence known as subsumption. The resulting structure is displayed as a series of hierarchical menus. When generated from a set of retrieved documents, a user browsing the menus gains an overview of their content in a manner distinct from existing techniques. The methods used to build the structure are simple and appear to be effective. The formation and presentation of the hierarchy is described along with a study of some of its properties, including a preliminary experiment, which indicates that users may find the hierarchy a more efficient means of locating relevant documents than the classic method of scanning a ranked document list

0.045347005 = product of:
  0.11336751 = sum of:
    0.018193537 = weight(_text_:of in 5690) [ClassicSimilarity], result of:
      0.018193537 = score(doc=5690,freq=6.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.23940048 = fieldWeight in 5690, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=5690)
    0.09517398 = weight(_text_:subject in 5690) [ClassicSimilarity], result of:
      0.09517398 = score(doc=5690,freq=6.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.5475522 = fieldWeight in 5690, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0625 = fieldNorm(doc=5690)
  0.4 = coord(2/5)

Abstract

Discusses some of the methods of subject access to on-line catalohues (OPACs) and argues that none are entirley satisfactory. Describes 2 methods for improving subject access: best match searching; and automatic query expansion application and discusses their feasibility. Mentions emerging application standards for information retrieval and concludes that existing standards are incompatible with most methods for improving standards

Source

OPACs and the user: Proc. of the 3rd Anglo-Nordic seminar, Apr. 90

0.044106636 = product of:
  0.11026659 = sum of:
    0.013130054 = weight(_text_:of in 5346) [ClassicSimilarity], result of:
      0.013130054 = score(doc=5346,freq=2.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.17277241 = fieldWeight in 5346, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.078125 = fieldNorm(doc=5346)
    0.097136535 = weight(_text_:subject in 5346) [ClassicSimilarity], result of:
      0.097136535 = score(doc=5346,freq=4.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.55884314 = fieldWeight in 5346, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.078125 = fieldNorm(doc=5346)
  0.4 = coord(2/5)

Abstract

Teil von: Final Report to the ALCTS/CCS Subject Analysis Committee. June 1997 (http://web2.ala.org/ala/alctscontent/CCS/committees/subjectanalysis/subjectrelations/finalreport.cfm).

0.043431304 = product of:
  0.072385505 = sum of:
    0.019651264 = weight(_text_:of in 175) [ClassicSimilarity], result of:
      0.019651264 = score(doc=175,freq=28.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.25858206 = fieldWeight in 175, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=175)
    0.02747436 = weight(_text_:subject in 175) [ClassicSimilarity], result of:
      0.02747436 = score(doc=175,freq=2.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.15806471 = fieldWeight in 175, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03125 = fieldNorm(doc=175)
    0.02525988 = product of:
      0.05051976 = sum of:
        0.05051976 = weight(_text_:headings in 175) [ClassicSimilarity], result of:
          0.05051976 = score(doc=175,freq=2.0), product of:
            0.23569997 = queryWeight, product of:
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.04859849 = queryNorm
            0.21433927 = fieldWeight in 175, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.03125 = fieldNorm(doc=175)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

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 demonstration of a research prototype illustrates a matching function for compound descriptors, or multi-concept subject headings, that does not rely on 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 on a measure of semantic closeness between terms.The work is part of the EPSRC funded FACET project in collaboration with the UK National Museum of Science and Industry (NMSI) which includes the National Railway Museum. An export of NMSI's Collections Database is used as the dataset for the research. The J. Paul Getty Trust's Art and Architecture Thesaurus (AAT) is the main thesaurus in the project. The AAT is a widely used thesaurus (over 120,000 terms). Descriptors are organised in 7 facets representing separate conceptual classes of terms.The FACET application is a multi tiered architecture accessing a SQL Server database, with an OLE DB connection. The thesauri are stored as relational tables in the Server's database. However, a key component of the system is a parallel representation of the underlying semantic network as an in-memory structure of thesaurus concepts (corresponding to preferred terms). The structure models the hierarchical and associative interrelationships of thesaurus concepts via weighted poly-hierarchical links. Its primary purpose is real-time semantic expansion of query terms, achieved by a spreading activation semantic closeness algorithm. Queries with associated results are stored persistently using XML format data. A Visual Basic interface combines a thesaurus browser and an initial term search facility that takes into account equivalence relationships. Terms are dragged to a direct manipulation Query Builder which maintains the facet structure.

Source

Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries : JCDL 2002 ; July 14 - 18, 2002, Portland, Oregon, USA. Ed. by Gary Marchionini

0.036283102 = product of:
  0.09070775 = sum of:
    0.07213905 = weight(_text_:list in 4575) [ClassicSimilarity], result of:
      0.07213905 = score(doc=4575,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.2863593 = fieldWeight in 4575, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4575)
    0.0185687 = weight(_text_:of in 4575) [ClassicSimilarity], result of:
      0.0185687 = score(doc=4575,freq=16.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.24433708 = fieldWeight in 4575, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4575)
  0.4 = coord(2/5)

Abstract

The Web provides a massive knowledge source, as do intranets and other electronic document collections. However, much of that knowledge is encoded implicitly and cannot be applied directly without processing into some more appropriate structures. Searching, browsing, question answering, for example, could all benefit from domain-specific knowledge contained in the documents, and in applications such as simple search we do not actually need very "deep" knowledge structures such as ontologies, but we can get a long way with a model of the domain that consists of term hierarchies. We combine domain knowledge automatically acquired by exploiting the documents' markup structure with knowledge extracted an the fly to assist a user with ad hoc search requests. Such a search system can suggest query modification options derived from the actual data and thus guide a user through the space of documents. This article gives a detailed account of a task-based evaluation that compares a search system that uses the outlined domain knowledge with a standard search system. We found that users do use the query modification suggestions proposed by the system. The main conclusion we can draw from this evaluation, however, is that users prefer a system that can suggest query modifications over a standard search engine, which simply presents a ranked list of documents. Most interestingly, we observe this user preference despite the fact that the baseline system even performs slightly better under certain criteria.

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.13, S.1377-1393

0.03580339 = product of:
  0.089508474 = sum of:
    0.07213905 = weight(_text_:list in 2929) [ClassicSimilarity], result of:
      0.07213905 = score(doc=2929,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.2863593 = fieldWeight in 2929, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2929)
    0.017369429 = weight(_text_:of in 2929) [ClassicSimilarity], result of:
      0.017369429 = score(doc=2929,freq=14.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.22855641 = fieldWeight in 2929, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2929)
  0.4 = coord(2/5)

Abstract

Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state-of-the-art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.6, S.1345-1357

0.03485462 = product of:
  0.08713655 = sum of:
    0.015756065 = weight(_text_:of in 178) [ClassicSimilarity], result of:
      0.015756065 = score(doc=178,freq=8.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.20732689 = fieldWeight in 178, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=178)
    0.07138049 = weight(_text_:subject in 178) [ClassicSimilarity], result of:
      0.07138049 = score(doc=178,freq=6.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.41066417 = fieldWeight in 178, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=178)
  0.4 = coord(2/5)

Abstract

This paper describes a study that explored ways in which users' subject-searching problems in a local online catalog might be reduced. On a weekly basis, the author reviewed catalog transaction logs to identify topics of subject searches retrieving no records for which appropriate information resources may actually be represented in the catalog. For topics thus identified, the author explored two potential ameliorations of the no-hits search results through the use of authority record cross-references and pathfinder records providing brief instructions on search refinement. This paper describes the study findings, discusses possible concerns regarding the amelioration methods used, outlines additional steps needed to determine whether the potential ameliorations make a difference to users' searching experiences, and suggests related areas for further research.

0.03356597 = product of:
  0.08391492 = sum of:
    0.015919344 = weight(_text_:of in 42) [ClassicSimilarity], result of:
      0.015919344 = score(doc=42,freq=6.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.20947541 = fieldWeight in 42, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=42)
    0.06799558 = weight(_text_:subject in 42) [ClassicSimilarity], result of:
      0.06799558 = score(doc=42,freq=4.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.3911902 = fieldWeight in 42, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0546875 = fieldNorm(doc=42)
  0.4 = coord(2/5)

Abstract

Parent-Child relationships in MeSH trees were surveyed and described, and their patterns in the relational structure were determined for selected broad subject categories and subcategories. Is-a relationships dominated and were more prevalent overall than previously reported; however, an additional 67 different relationships were also seen, most of them nonhierarchical. Relational profiles were found to vary both within and among subject subdomains, but tended to display characteristic domain patterns. The implications for inferential reasoning and other cognitive and computational operations on hierarchical structures are considered

Source

Structures and relations in knowledge organization: Proceedings of the 5th International ISKO-Conference, Lille, 25.-29.8.1998. Ed.: W. Mustafa el Hadi et al

0.033159617 = product of:
  0.08289904 = sum of:
    0.057711232 = weight(_text_:list in 4992) [ClassicSimilarity], result of:
      0.057711232 = score(doc=4992,freq=2.0), product of:
        0.25191793 = queryWeight, product of:
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.04859849 = queryNorm
        0.22908744 = fieldWeight in 4992, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.183657 = idf(docFreq=673, maxDocs=44218)
          0.03125 = fieldNorm(doc=4992)
    0.025187809 = weight(_text_:of in 4992) [ClassicSimilarity], result of:
      0.025187809 = score(doc=4992,freq=46.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.33143494 = fieldWeight in 4992, product of:
          6.78233 = tf(freq=46.0), with freq of:
            46.0 = termFreq=46.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=4992)
  0.4 = coord(2/5)

Abstract

The field of human information behavior runs the gamut of processes from the realization of a need or gap in understanding, to the search for information from one or more sources to fill that gap, to the use of that information to complete a task at hand or to satisfy a curiosity, as well as other behaviors such as avoiding information or finding information serendipitously. Designers of mechanisms, tools, and computer-based systems to facilitate this seeking and search process often lack a full knowledge of the context surrounding the search. This context may vary depending on the job or role of the person; individual characteristics such as personality, domain knowledge, age, gender, perception of self, etc.; the task at hand; the source and the channel and their degree of accessibility and usability; and the relationship that the seeker shares with the source. Yet researchers have yet to agree on what context really means. While there have been various research studies incorporating context, and biennial conferences on context in information behavior, there lacks a clear definition of what context is, what its boundaries are, and what elements and variables comprise context. In this book, we look at the many definitions of and the theoretical and empirical studies on context, and I attempt to map the conceptual space of context in information behavior. I propose theoretical frameworks to map the boundaries, elements, and variables of context. I then discuss how to incorporate these frameworks and variables in the design of research studies on context. We then arrive at a unified definition of context. This book should provide designers of search systems a better understanding of context as they seek to meet the needs and demands of information seekers. It will be an important resource for researchers in Library and Information Science, especially doctoral students looking for one resource that covers an exhaustive range of the most current literature related to context, the best selection of classics, and a synthesis of these into theoretical frameworks and a unified definition. The book should help to move forward research in the field by clarifying the elements, variables, and views that are pertinent. In particular, the list of elements to be considered, and the variables associated with each element will be extremely useful to researchers wanting to include the influences of context in their studies.

0.03209767 = product of:
  0.080244176 = sum of:
    0.020760437 = weight(_text_:of in 5691) [ClassicSimilarity], result of:
      0.020760437 = score(doc=5691,freq=20.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.27317715 = fieldWeight in 5691, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5691)
    0.059483737 = weight(_text_:subject in 5691) [ClassicSimilarity], result of:
      0.059483737 = score(doc=5691,freq=6.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.34222013 = fieldWeight in 5691, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5691)
  0.4 = coord(2/5)

Abstract

The second half of a 'before and after' study to evaluate the impact of an online catalogue on subject searching behaviour is reported. A holistic approach is adopted encompassing both catalogue use and browsing at the shelves for catalogue users and non-users. Verbal and non-verbal data were elicited from searchers using a combined methodology including talk-aloud technique, observation and a screen logging facility. An extensive qualitative analysis was carried out correlating expressed topics, search formulation strategies and documents retrieved at the shelves. The online catalogue environment does not appear to have increased the extent of subject searching nor the use of the bibliographic tool. The manual PRECIS index supported a contextual approach for broad and more interactive search formulations whereas the OPAC encouraged a matching approach and narrow formulations with fewer but user generated formulations. The success rate of the online catalogue was slightly better than that of the manual tools but fewer items were retrieved at the shelves. Non-users of the bibliographic tools seemed to be just as successful. To improve retrieval effectiveness it is suggested that online catalogues should cater for both matching and contextual approaches to searching. Recent research indicates that a more interactive process could be promoted by providing query expansion through a combination of searching aids for matching, for search formulation assistance and for structured contextual retrieval

Source

Journal of documentation. 46(1990), S.318-338

0.031374592 = product of:
  0.07843648 = sum of:
    0.023487754 = weight(_text_:of in 4847) [ClassicSimilarity], result of:
      0.023487754 = score(doc=4847,freq=10.0), product of:
        0.07599624 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04859849 = queryNorm
        0.3090647 = fieldWeight in 4847, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=4847)
    0.05494872 = weight(_text_:subject in 4847) [ClassicSimilarity], result of:
      0.05494872 = score(doc=4847,freq=2.0), product of:
        0.17381717 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.04859849 = queryNorm
        0.31612942 = fieldWeight in 4847, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0625 = fieldNorm(doc=4847)
  0.4 = coord(2/5)

Abstract

State of the art review of query expansion (or term expansion) as the process of supplementing the original query with additional terms in order to improve retrieval performance. Research in the subject is presented in a highly structured way and is presented according to 3 types of query expansion; manual query expansion; automatic query expansion; and interactive query expansion

Source

Annual review of information science and technology. 31(1996), S.121-187