Exploring emerging knowledge landscapes through preprints: a DIKW-based computational framework for transforming data into actionable scholarly insights
DOI:
https://doi.org/10.47989/ir31ISIC65160Keywords:
Preprints, DIKW hierarchy, Knowledge Structures, Knowledge Structures; Semantic and Thematic ModelingAbstract
Introduction. Preprints offer a critical vantage point for tracing emerging knowledge trajectories but remain underexamined. Within the data–information–knowledge–wisdom (DIKW) framework, preprints are positioned as raw data requiring computational transformation into interpretable knowledge.
Method. Using explainable artificial intelligence (XAI) preprints as a case domain, this study applied a computational knowledge transformation framework. After a two-stage quality filter ensuring preprints’ metadata completeness and institutional validity, latent Dirichlet allocation (LDA) extracted thematic structures, Word2Vec analysed semantic relations, and content analysis refined interpretive coherence.
Analysis. The framework operationalised the DIKW hierarchy by tracing how unstructured preprint texts evolved into structured information and interconnected knowledge. LDA identified thematic clusters, while Word2Vec and content analysis revealed semantic proximity and conceptual cohesion, forming an interpretable knowledge network.
Results. Two thematic trajectories were identified: (1) algorithmic development and model optimisation, highlighting methodological innovation; and (2) user-centred evaluation and performance assessment, emphasising interpretability and user interaction.
Conclusion. With appropriate quality control and computational modelling, preprints can serve as an early-warning system for emerging knowledge and technologies, offering valuable insights for scholars and data scientists.
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