STA-EFM: Semantic–Temporal–Authority Evidence Fusion for Multi-Source Emerging Technology Information Processing and Forecasting

Future Internet ecosystems increasingly rely on distributed and intelligent data infrastructures, yet evidence of their evolution is fragmented across scholarly publications, patents, open-source repositories, and technology news. This study proposes the Semantic–Temporal–Authority Evidence Fusion Model (STA-EFM), a multi-source information-processing framework for identifying, ranking, and forecasting emerging data-infrastructure trajectories relevant to Future Internet ecosystems. STA-EFM integrates arXiv, PatentsView/USPTO, and GitHub records, with GDELT providing a limited complementary technology-news signal, harmonizes heterogeneous metadata, and assigns records to ten predefined technology classes using Sentence-BERT prototype similarity. A manually reviewed sample of 500 records was used to validate semantic relevance and taxonomy assignment. Source-normalized semantic evidence was combined with temporal activity, growth, momentum, recency, and source-specific authority indicators to generate monthly STA scores. A semantic threshold of 0.60 achieved an accuracy of 0.8800 and an F1-score of 0.9088, while taxonomy assignment achieved 0.8830 accuracy. Source-aware filtering reduced 2,069,979 records to 3337 relevant records. Federated Data Architectures achieved the highest mean STA score 0.3970, followed by Digital Twin Infrastructure and Graph and Vector Databases. Across the tested STA-level weighting configurations, Spearman rank correlations with the baseline ranged from 0.8667 to 0.9879. The framework provides an interpretable and traceable mechanism for monitoring emerging Future Internet infrastructure technologies.