Search (3 results, page 1 of 1)

  • × author_ss:"Greenberg, J."
  • × year_i:[2020 TO 2030}
  1. Grabus, S.; Logan, P.M.; Greenberg, J.: Temporal concept drift and alignment : an empirical approach to comparing knowledge organization systems over time (2022) 0.01 
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    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.
    Content
    Vgl.: https://www.nomos-elibrary.de/10.5771/0943-7444-2022-2/ko-knowledge-organization-jahrgang-49-2022-heft-2?page=1.
    Source
    Knowledge organization. 49(2022) no.2, S.69 - 78
  2. Greenberg, J.; Zhao, X.; Monselise, M.; Grabus, S.; Boone, J.: Knowledge organization systems : a network for AI with helping interdisciplinary vocabulary engineering (2021) 0.01 
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    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.
  3. Li, K.; Greenberg, J.; Dunic, J.: Data objects and documenting scientific processes : an analysis of data events in biodiversity data papers (2020) 0.00 
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    Abstract
    The data paper, an emerging scholarly genre, describes research data sets and is intended to bridge the gap between the publication of research data and scientific articles. Research examining how data papers report data events, such as data transactions and manipulations, is limited. The research reported on in this article addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility. Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space. The findings challenge the degrees to which data papers are a distinct genre compared to research articles and they describe data-centric research processes in a through way. This article also discusses how our results could inform a better data publication ecosystem in the future.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.2, S.172-182

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