Search (30 results, page 2 of 2)

0.0014873719 = product of:
  0.008924231 = sum of:
    0.008924231 = weight(_text_:in in 144) [ClassicSimilarity], result of:
      0.008924231 = score(doc=144,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.15028831 = fieldWeight in 144, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.078125 = fieldNorm(doc=144)
  0.16666667 = coord(1/6)

Series

Advances in knowledge organization; vol.17

0.0014724231 = product of:
  0.008834538 = sum of:
    0.008834538 = weight(_text_:in in 579) [ClassicSimilarity], result of:
      0.008834538 = score(doc=579,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.14877784 = fieldWeight in 579, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=579)
  0.16666667 = coord(1/6)

Abstract

This article presents a logical analysis of the characteristics of indexing and their effects on retrieval performance.It establishes the ability to ask the questions one needs to ask as the foundation of performance evaluation, and recall and discrimination as the basic quantitative performance measures for binary noninteractive retrieval systems. It then defines the characteristics of indexing that affect retrieval - namely, indexing devices, viewpoint-based and importance-based indexing exhaustivity, indexing specifity, indexing correctness, and indexing consistency - and examines in detail their effects on retrieval. It concludes that retrieval performance depends chiefly on the match between indexing and the requirements of the individual query and on the adaption of the query formulation to the characteristics of the retrieval system, and that the ensuing complexity must be considered in the design and testing of retrieval systems

0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 5449) [ClassicSimilarity], result of:
      0.007728611 = score(doc=5449,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 5449, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5449)
  0.16666667 = coord(1/6)

Abstract

As an attempt to make a general structural theory of information retrieval, a documentation system (DS) is defined as a formal system consisting of (a) a set o of objects (documents); (b) a set A++ of elementary attributes (key-words), from which further attributes may be constructed: A++ generates A; (c) a set of axioms of the form X++(x)=m (m¯M, M a set of constant connecting attributes with objects: from the axioms further theorems (=true statements) may be constructed. By use of the theorems, different mappings O -> P(o) (P(o) set of all subsets of o) (search question -> set of documents retrieved) are defined. The type of a DS depends on two basic decisions: (1) choice of the rules for the construction of attributes and theorems, e.g., logical product in coordinate indexing; links. (2) choice of M; M may consist of the two constants 'applicable' and 'not applicable', or some positive integers, ...; Further practical decisions: A++ hierarchical or not; kind of mapping; introduction of roles (=further attributes). The most simple case - ordinary two-valued Coordinate Indexing - is discusssed in detail; o is a free distributive (but not Boolean) lattice, the homographic image a ring of subsets of o; instead of negation which is not useful, a useful retrieval operation 'praeternagation' is introduced. Furthermore these are discussed: a generalized definition of superimposed coding, some functions for the distance of objects or attributes; optimization and automatic derivation of classifications. The model takes into account term-term relations and document-document relations. It may serve as a structural framework in terms of which the functional problems of retrieval theory may be expressed more clearly

0.0011898974 = product of:
  0.0071393843 = sum of:
    0.0071393843 = weight(_text_:in in 1864) [ClassicSimilarity], result of:
      0.0071393843 = score(doc=1864,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.120230645 = fieldWeight in 1864, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=1864)
  0.16666667 = coord(1/6)

Abstract

Critical comments on the BSO concern basic principles used in system construction, the quality of which, from the standpoint of the author, does not meet the requirements set out for the BSO. First of all, it would be necessary to revise and clearly formulate the purpose of the system, to make and consistently implement a basic decision on its structural characteristics, to provide for high-quality conceptual analysis and subject expertise which directly influence the content of the tables, and to clearly display supplemetary materials for the tables

0.0011898974 = product of:
  0.0071393843 = sum of:
    0.0071393843 = weight(_text_:in in 7960) [ClassicSimilarity], result of:
      0.0071393843 = score(doc=7960,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.120230645 = fieldWeight in 7960, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=7960)
  0.16666667 = coord(1/6)

Abstract

Reports on part of a study of real users' behaviour in selecting documents from a list of citations resulting from a search of an information retrieval system. Document selection involves value judgements and decision making. Understanding how users evaluate documents and make decisions provides a basis for designing intelligent information retrieval system that can do a better job of predicting usefulness

0.0010517307 = product of:
  0.006310384 = sum of:
    0.006310384 = weight(_text_:in in 527) [ClassicSimilarity], result of:
      0.006310384 = score(doc=527,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.10626988 = fieldWeight in 527, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=527)
  0.16666667 = coord(1/6)

Abstract

Synthesizing and building on many ideas from the literature, this article presents an improved conceptual framework that clarifies the notion of relevance with its many elements, variables, criteria, and situational factors. Relevance is defined as a Relationship (R) between an Information Object (I) and an Information Need (N) (which consists of Topic, User, Problem/Task, and Situation/Context) with focus on R. This defines Relevance-as-is (conceptual relevance, strong relevance). To determine relevance, an Agent A (a person or system) operates on a representation I? of the information object and a representation N? of the information need, resulting in relevance-as-determined (operational measure of relevance, weak relevance, an approximation). Retrieval tests compare relevance-as-determined by different agents. This article discusses and compares two major approaches to conceptualizing relevance: the entity-focused approach (focus on elaborating the entities involved in relevance) and the relationship-focused approach (focus on explicating the relational nature of relevance). The article argues that because relevance is fundamentally a relational construct the relationship-focused approach deserves a higher priority and more attention than it has received. The article further elaborates on the elements of the framework with a focus on clarifying several critical issues on the discourse on relevance.

0.0010517307 = product of:
  0.006310384 = sum of:
    0.006310384 = weight(_text_:in in 2127) [ClassicSimilarity], result of:
      0.006310384 = score(doc=2127,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.10626988 = fieldWeight in 2127, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2127)
  0.16666667 = coord(1/6)

Abstract

This study demonstrates an improved conceptual foundation to support well-structured analysis of image topicality. First we present a conceptual framework for analyzing image topicality, explicating the layers, the perspectives, and the topical relevance relationships involved in modeling the topicality of art images. We adapt a generic relevance typology to image analysis by extending it with definitions and relationships specific to the visual art domain and integrating it with schemes of image-text relationships that are important for image subject indexing. We then apply the adapted typology to analyze the topical relevance relationships between 11 art images and 768 image tags assigned by art historians and librarians. The original contribution of our work is the topical structure analysis of image tags that allows the viewer to more easily grasp the content, context, and meaning of an image and quickly tune into aspects of interest; it could also guide both the indexer and the searcher to specify image tags/descriptors in a more systematic and precise manner and thus improve the match between the two parties. An additional contribution is systematically examining and integrating the variety of image-text relationships from a relevance perspective. The paper concludes with implications for relational indexing and social tagging.

0.0010517307 = product of:
  0.006310384 = sum of:
    0.006310384 = weight(_text_:in in 5614) [ClassicSimilarity], result of:
      0.006310384 = score(doc=5614,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.10626988 = fieldWeight in 5614, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5614)
  0.16666667 = coord(1/6)

Abstract

Throughout an information search, a user needs to make sense of the information found to create an understanding. This requires cognitive effort that can be demanding. Building on prior sensemaking models and expanding them with ideas from learning and cognitive psychology, we examined the use of cognitive mechanisms during individual sensemaking. We conducted a qualitative user study of 15 students who searched for and made sense of information for business analysis and news writing tasks. Through the analysis of think-aloud protocols, recordings of screen movements, intermediate work products of sensemaking, including notes and concept maps, and final reports, we observed the use of 17 data-driven and structure-driven mechanisms for processing new information, examining individual concepts and relationships, and detecting anomalies. These cognitive mechanisms, as the basic operators that move sensemaking forward, provide in-depth understanding of how people process information to produce sense. Meaningful learning and sensemaking are closely related, so our findings apply to learning as well. Our results contribute to a better understanding of the sensemaking process-how people think-and this better understanding can inform the teaching of thinking skills and the design of improved sensemaking assistants and mind tools.

7.4368593E-4 = product of:
  0.0044621155 = sum of:
    0.0044621155 = weight(_text_:in in 672) [ClassicSimilarity], result of:
      0.0044621155 = score(doc=672,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.07514416 = fieldWeight in 672, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=672)
  0.16666667 = coord(1/6)

Abstract

This chapter describes not so much what digital libraries are but what digital libraries with semantic support could and should be. It discusses the nature of Knowledge Organization Systems (KOS) and how KOS can support digital library users. It projects a vision for designers to make and for users to demand better digital libraries. What is a digital library? The term \Digital Library" (DL) is used to refer to a range of systems, from digital object and metadata repositories, reference-linking systems, archives, and content management systems to complex systems that integrate advanced digital library services and support for research and practice communities. A DL may offer many technology-enabled functions and services that support users, both as information producers and as information users. Many of these functions appear in information systems that would not normally be considered digital libraries, making boundaries even more blurry. Instead of pursuing the hopeless quest of coming up with the definition of digital library, we present a framework that allows a clear and somewhat standardized description of any information system so that users can select the system(s) that best meet their requirements. Section 2 gives a broad outline for more detail see the DELOS DL Reference Model.

7.4368593E-4 = product of:
  0.0044621155 = sum of:
    0.0044621155 = weight(_text_:in in 2293) [ClassicSimilarity], result of:
      0.0044621155 = score(doc=2293,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.07514416 = fieldWeight in 2293, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2293)
  0.16666667 = coord(1/6)

Abstract

We illustrate the principle of unified treatment of all authority data for any kind of entities, subjects/topics, places, events, persons, organizations, etc. through the design and implementation of an enriched authority database for organizations, maintained as an integral part of an authority database that also includes subject authority control / classification data, using the same structures for data and common modules for processing and display of data. Organization-related data are stored in information systems of many companies. We specifically examine the case of the World Bank Group (WBG) according to organization role: suppliers, partners, customers, competitors, authors, publishers, or subjects of documents, loan recipients, suppliers for WBG-funded projects and subunits of the organization itself. A central organization authority where each organization is identified by a URI, represented by several names and linked to other organizations through hierarchical and other relationships serves to link data from these disparate information systems. Designing the conceptual structure of a unified authority database requires integrating SKOS, the W3C Organization Ontology and other schemes into one comprehensive ontology. To populate the authority database with organizations, we import data from external sources (e.g., DBpedia and Library of Congress authorities) and internal sources (e.g., the lists of organizations from multiple WBG information systems).