Forecasting the Evolution of Scientific Knowledge: A Dynamic Knowledge Graph Approach Integrating Temporal Embeddings and Large Language Models
摘要
The exponential proliferation of scientific literature presents formidable impediments to comprehensive understanding and strategic anticipation of future research trajectories across diverse domains. While extant computational methodologies, including bibliometrics and static topic modeling, facilitate the analysis of prevailing trends and thematic structures, the rigorous forecasting of the complex structural evolution inherent in the diachronic development of scientific knowledge remains a largely unresolved challenge. Addressing this lacuna, we introduce DynSciGraph, a computational framework engineered to harness the semantic interpretation capabilities of Large Language Models (LLMs) for the high-fidelity extraction of fine-grained conceptual entities and semantic relations from extensive corpora of scholarly publications. The framework subsequently leverages Temporal Graph Neural Networks (TGNNs) to model the non-linear dynamics intrinsic to time-evolving, domain-specific knowledge graphs (KGs), coupled with sequence models for multi-horizon predictive tasks. A distinctive architectural feature of DynSciGraph is its capacity to integrate heterogeneous external signals encompassing bibliometric indicators, research funding allocation patterns, and patenting activities, thereby augmenting the contextual richness of its forecasts. The evidence reported in this manuscript is exclusively simulation-based and does not yet demonstrate real-world forecasting performance. We present a detailed exposition of the architectural design and report proof-of-concept simulation results on synthetic corpora parameterized to reflect characteristics of Materials Science and Artificial Intelligence domains. All reported quantitative outcomes derive from a controlled simulation environment whose generative process is fully specified in Sect.