Unveiling Knowledge Organization Systems’ Artifacts for Digital Agriculture with Lexical Network Analysis

Soares, F.M. ✉; Bergier, I.; Coradini, M.C.; Ferreira, A.P.L.; Telles, M.A.; dos Santos Maculan, B.C.M.; de Cléofas Faggion Alencar, M.; Simão, V.P.M.; de Almeida, B.T.; Drucker, D.P.; Machado Vieira, M.S.; da Cruz, S.M.S.

Angol nyelvű Szakcikk (Folyóiratcikk) Tudományos
Megjelent: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE 0302-9743 14319 LNCS pp. 299-311 2023
    Azonosítók
    This article presents a bibliometric and terminological study of a corpus composed of abstracts and titles of 278 articles retrieved by a review protocol planned for surveying initiatives on building artifacts for modeling knowledge related to agricultural production systems. The original corpus comprised a 53,379-word linguistic extract filtered to 111 interconnected major terminologies by combining AntConc and VOSViewer tools. The reduced data were imported into the Gephi tool for analysis of lexical network graphs. Emergent clusters and their central terms underscore the thematic areas that prominently shape the landscape of agricultural Knowledge Organization Systems (KOS) and highlight the interplay between technological advancements, semantic enrichment, and domain-specific challenges. Our analysis of term occurrences and clusters contributes to a broader understanding of these concepts, inferring their significance, roles, and interconnections within the agricultural landscape. It also sheds light on the roles played by KOS in Digital Agriculture. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
    Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
    2026-08-07 21:08