Search (2 results, page 1 of 1)

  • × author_ss:"Huang, X."
  • × author_ss:"Soergel, D."
  1. Huang, X.; Soergel, D.: Relevance: an improved framework for explicating the notion (2013) 0.00 
    0.0025370158 = product of:
  0.0050740317 = sum of:
    0.0050740317 = product of:
      0.010148063 = sum of:
        0.010148063 = weight(_text_:a in 527) [ClassicSimilarity], result of:
          0.010148063 = score(doc=527,freq=18.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.19109234 = fieldWeight in 527, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=527)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    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.
    Type
    a
  2. Huang, X.; Soergel, D.; Klavans, J.L.: Modeling and analyzing the topicality of art images (2015) 0.00 
    0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 2127) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=2127,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 2127, 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=2127)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    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.
    Type
    a