Search (13 results, page 1 of 1)

0.0039819763 = product of:
  0.011945928 = sum of:
    0.011945928 = weight(_text_:a in 5279) [ClassicSimilarity], result of:
      0.011945928 = score(doc=5279,freq=18.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.22931081 = fieldWeight in 5279, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=5279)
  0.33333334 = coord(1/3)

Abstract

This paper demonstrates the application of conventional database design techniques to thesaurus representation. The thesaurus is considered as a printed document, as a semantic net, and as a relational database to be used in conjunction with an intelligent information retrieval system. Some issues raised by analysis of two standard thesauri include: the prevalence of compound terms and the representation of term structure; thesaurus redundancy and the extent to which it can be eliminated in machine-readable versions; the difficulty of exploiting thesaurus knowledge originally designed for human rather than automatic interpretation; deriving 'strength of association' measures between terms in a thesaurus considered as a semantic net; facet representation and the need for variations in the data model to cater for structural differences between thesauri. A complete schema of database tables is presented, with an outline suggestion for using the stored information when matching one or more thesaurus terms with a user's query

Type

a

0.0030970925 = product of:
  0.009291277 = sum of:
    0.009291277 = weight(_text_:a in 5806) [ClassicSimilarity], result of:
      0.009291277 = score(doc=5806,freq=8.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.17835285 = fieldWeight in 5806, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5806)
  0.33333334 = coord(1/3)

Abstract

Reports on work in progress to define an object oriented model of a probabilistic information retrieval system (OKAPI), the central component of which is the query itself. Considers how to represent queries both internally and at the user interface level, and their relationship with other components of the model. The model will form the basis of a configurable user interface, which allows controlled experiments to be undertaken, and could be adapted to the needs of different users accessing different databases. Implementation will involve the use of a high level interpreted scripting language for overall control, communicating with an internal model and an interface model, designed and developed using object oriented techniques

Type

a

0.0030970925 = product of:
  0.009291277 = sum of:
    0.009291277 = weight(_text_:a in 7446) [ClassicSimilarity], result of:
      0.009291277 = score(doc=7446,freq=8.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.17835285 = fieldWeight in 7446, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=7446)
  0.33333334 = coord(1/3)

Abstract

Discusses the use of transaction logging in Okapi related projects to allow search algorithms and user interfaces to be investigated and compared. Notes the variety of purposes for which transaction logging is undertaken (recovery, playback or analysis), and the corresponding variety of methods and formats employed. Presents a series of examples illustrating logging for both character based and GUI systems, and demonstrating the usefulness of a relational DMBS for storing and analyzing the data. Notes the planned development of logging techniques for distributed versions of Okapi

Footnote

Contribution to a thematic issue on Okapi and information retrieval research

Type

a

0.0030970925 = product of:
  0.009291277 = sum of:
    0.009291277 = weight(_text_:a in 430) [ClassicSimilarity], result of:
      0.009291277 = score(doc=430,freq=8.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.17835285 = fieldWeight in 430, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=430)
  0.33333334 = coord(1/3)

Abstract

Explores interface design raised by the development and evaluation of Okapi, a highly interactive information retrieval system based on a probabilistic retrieval model with relevance feedback. It uses terms frequency weighting functions to display retrieved items in a best match ranked order; it can also find additional items similar to those marked as relevant by the searcher. Compares the effectiveness of automatic and interactive query expansion in different user interface environments. focuses on the nature of interaction in information retrieval and the interrelationship between functional visibility, the user's cognitive loading and the balance of control between user and system

Type

a

0.0030970925 = product of:
  0.009291277 = sum of:
    0.009291277 = weight(_text_:a in 4705) [ClassicSimilarity], result of:
      0.009291277 = score(doc=4705,freq=8.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.17835285 = fieldWeight in 4705, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4705)
  0.33333334 = coord(1/3)

Abstract

Describes the evolution and some of the functions of the layers of complex software which lie between the basic formula at the heart of the Okapi information retrieval system and the simple interface which it presents to the user. Addresses some possible approaches to the goal of easy system configurability, a goal which can be at odds with the desire to build a fully integrated package. Considers some of the issues to be faced in moving Okapi to the WWW, client server, distributed processing environment

Footnote

Contribution to a thematic issue on Okapi and information retrieval research

Type

a

0.00296799 = product of:
  0.00890397 = sum of:
    0.00890397 = weight(_text_:a in 3369) [ClassicSimilarity], result of:
      0.00890397 = score(doc=3369,freq=10.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.1709182 = fieldWeight in 3369, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=3369)
  0.33333334 = coord(1/3)

Abstract

The ENQUIRE project forms part of a series of investigations on query expansion in the Okapi experimental text retrieval system. A configurable user interface was implemented as an evaluative tool and tested in two locations on two different databases: the library catalogue of The London Business SChool and the computing section of INSPEC. The system offered a range of possible strategies based on thesaural terms for reformulating queries. These could be initiated automatically by the system or interactively with the user. The formative phase of the evaluation established the appropriateness and usability of the interface as well as users' perceptions of the underlying functionality. The aim of the large scale field trial was to determine to what extent user would select thesaural terms suggested by the system to reformulate queries, and to evaluate the effectiveness of a new dynamic form of query expansion implemented for this project

0.002473325 = product of:
  0.0074199745 = sum of:
    0.0074199745 = weight(_text_:a in 5354) [ClassicSimilarity], result of:
      0.0074199745 = score(doc=5354,freq=10.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.14243183 = fieldWeight in 5354, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5354)
  0.33333334 = coord(1/3)

Abstract

Human Information Behavior (HIB) research commonly examines behavior in the context of why information is acquired and how it will be used, but usually at the level of the work or everyday-life tasks the information will support. HIB has not been examined in detail at the broader contextual level of intellectual purpose (that is, the higher-order conceptual tasks the information was acquired to support). Examination at this level can enhance holistic understanding of HIB as a "means to an intellectual end" and inform the design of digital information environments that support information interaction for specific intellectual purposes. We investigate information-based ideation (IBI) as a specific intellectual information acquisition and use context by conducting Critical Incident-style interviews with 10 game designers, focusing on how they interact with information to generate and develop creative design ideas. Our findings give rise to a framework of their ideation-focused HIB, which systems designers can leverage to reason about how best to support certain behaviors to drive design ideation. These findings emphasize the importance of intellectual purpose as a driver for acquisition and desired outcome of use.

Type

a

0.0021899752 = product of:
  0.0065699257 = sum of:
    0.0065699257 = weight(_text_:a in 7734) [ClassicSimilarity], result of:
      0.0065699257 = score(doc=7734,freq=4.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.12611452 = fieldWeight in 7734, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=7734)
  0.33333334 = coord(1/3)

Abstract

In principle, the 'knowledge' encoded in a thesaurus can be exploited in many ways to help users clarify their information needs and enhance query performance, but attempts to automate this process via AI techniques face many practical difficulties. In the short term it may be more useful to improve support for direct interactive use of thesauri. We discuss some of the issues which have arisen when building an interface for thesaurus navigation and query enhancement, drawing on logs and user feedback from ongoing small-scale experiments

Type

a

0.0021899752 = product of:
  0.0065699257 = sum of:
    0.0065699257 = weight(_text_:a in 180) [ClassicSimilarity], result of:
      0.0065699257 = score(doc=180,freq=4.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.12611452 = fieldWeight in 180, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=180)
  0.33333334 = coord(1/3)

Abstract

We discuss whether it is feasible to build intelligent rule- or weight-based algorithms into general-purpose software for interactive thesaurus navigation. We survey some approaches to the problem reported in the literature, particularly those involving the assignement of 'link weights' in a thesaurus network, and point out some problems of both principle and practice. We then describe investigations which entailed logging the behavior of thesaurus users and testing the effect of thesaurus-based query enhancement in an IR system using term weighting, in an attempt to identify successful strategies to incorporate into automatic procedures. The results cause us to question many of the assumptions made by previous researchers in this area

Type

a

0.0018771215 = product of:
  0.0056313644 = sum of:
    0.0056313644 = weight(_text_:a in 4435) [ClassicSimilarity], result of:
      0.0056313644 = score(doc=4435,freq=4.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.10809815 = fieldWeight in 4435, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=4435)
  0.33333334 = coord(1/3)

Type

a

0.0017697671 = product of:
  0.0053093014 = sum of:
    0.0053093014 = weight(_text_:a in 362) [ClassicSimilarity], result of:
      0.0053093014 = score(doc=362,freq=2.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.10191591 = fieldWeight in 362, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=362)
  0.33333334 = coord(1/3)

Type

a

0.001564268 = product of:
  0.004692804 = sum of:
    0.004692804 = weight(_text_:a in 601) [ClassicSimilarity], result of:
      0.004692804 = score(doc=601,freq=4.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.090081796 = fieldWeight in 601, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=601)
  0.33333334 = coord(1/3)

Abstract

This article describes an evaluation of the Kea automatic keyphrase extraction algorithm. Document keyphrases are conventionally used as concise descriptors of document content, and are increasingly used in novel ways, including document clustering, searching and browsing interfaces, and retrieval engines. However, it is costly and time consuming to manually assign keyphrases to documents, motivating the development of tools that automatically perform this function. Previous studies have evaluated Kea's performance by measuring its ability to identify author keywords and keyphrases, but this methodology has a number of well-known limitations. The results presented in this article are based on evaluations by human assessors of the quality and appropriateness of Kea keyphrases. The results indicate that, in general, Kea produces keyphrases that are rated positively by human assessors. However, typical Kea settings can degrade performance, particularly those relating to keyphrase length and domain specificity. We found that for some settings, Kea's performance is better than that of similar systems, and that Kea's ranking of extracted keyphrases is effective. We also determined that author-specified keyphrases appear to exhibit an inherent ranking, and that they are rated highly and therefore suitable for use in training and evaluation of automatic keyphrasing systems.

Type

a

0.0013273255 = product of:
  0.0039819763 = sum of:
    0.0039819763 = weight(_text_:a in 3186) [ClassicSimilarity], result of:
      0.0039819763 = score(doc=3186,freq=2.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.07643694 = fieldWeight in 3186, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=3186)
  0.33333334 = coord(1/3)

Type

a