Search (2 results, page 1 of 1)

  • × author_ss:"Greenberg, J."
  • × year_i:[2020 TO 2030}
  1. Greenberg, J.; Zhao, X.; Monselise, M.; Grabus, S.; Boone, J.: Knowledge organization systems : a network for AI with helping interdisciplinary vocabulary engineering (2021) 0.02 
    0.015078641 = product of:
  0.060314562 = sum of:
    0.060314562 = weight(_text_:digital in 719) [ClassicSimilarity], result of:
      0.060314562 = score(doc=719,freq=2.0), product of:
        0.19770671 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.050121464 = queryNorm
        0.30507088 = fieldWeight in 719, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.0546875 = fieldNorm(doc=719)
  0.25 = coord(1/4)
    Abstract
    Knowledge Organization Systems (KOS) as networks of knowledge have the potential to inform AI operations. This paper explores natural language processing and machine learning in the context of KOS and Helping Interdisciplinary Vocabulary Engineering (HIVE) technology. The paper presents three use cases: HIVE and Historical Knowledge Networks, HIVE for Materials Science (HIVE-4-MAT), and Using HIVE to Enhance and Explore Medical Ontologies. The background section reviews AI foundations, while the use cases provide a frame of reference for discussing current progress and implications of connecting KOS to AI in digital resource collections.
  2. Grabus, S.; Logan, P.M.; Greenberg, J.: Temporal concept drift and alignment : an empirical approach to comparing knowledge organization systems over time (2022) 0.00 
    0.004785695 = product of:
  0.01914278 = sum of:
    0.01914278 = weight(_text_:library in 1100) [ClassicSimilarity], result of:
      0.01914278 = score(doc=1100,freq=2.0), product of:
        0.1317883 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.050121464 = queryNorm
        0.14525402 = fieldWeight in 1100, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1100)
  0.25 = coord(1/4)
    Abstract
    This research explores temporal concept drift and temporal alignment in knowledge organization systems (KOS). A comparative analysis is pursued using the 1910 Library of Congress Subject Headings, 2020 FAST Topical, and automatic indexing. The use case involves a sample of 90 nineteenth-century Encyclopedia Britannica entries. The entries were indexed using two approaches: 1) full-text indexing; 2) Named Entity Recognition was performed upon the entries with Stanza, Stanford's NLP toolkit, and entities were automatically indexed with the Helping Interdisciplinary Vocabulary application (HIVE), using both 1910 LCSH and FAST Topical. The analysis focused on three goals: 1) identifying results that were exclusive to the 1910 LCSH output; 2) identifying terms in the exclusive set that have been deprecated from the contemporary LCSH, demonstrating temporal concept drift; and 3) exploring the historical significance of these deprecated terms. Results confirm that historical vocabularies can be used to generate anachronistic subject headings representing conceptual drift across time in KOS and historical resources. A methodological contribution is made demonstrating how to study changes in KOS over time and improve the contextualization historical humanities resources.

Authors