Reimagining China-US Relations Prediction: A Multi-modal, Knowledge-Driven Approach with KDSCINet

Statistical models and data driven models have achieved remarkable results in international relation forecasting. However, most of these models have several common drawbacks, including (i) rely on large amounts of expert knowledge, limiting the objectivity, applicability, usability, interpretability and sustainability of models, (ii) can only use structured unimodal data or cannot make full use of multimodal data. To address these two problems, we proposed a Knowledge-Driven neural network architecture that conducts Sample Convolution and Interaction, named KDSCINet, for China-US relation forecasting. Firstly, we filter events pertaining to China-US relations from the GDELT database. Then, we extract text descriptions and images from news articles and utilize the fine-tuned pre-trained model MKGformer to obtain embeddings. Finally we connect textual and image embeddings of the event with the structured event value in GDELT database through multi-head attention mechanism to generate time series data, which is then feed into KDSCINet for China-US relation forecasting. Our approach enhances prediction accuracy by establishing a knowledge-driven temporal forecasting model that combines structured data, textual data and image data. Experiments demonstrate that KDSCINet can (i) outperform state-of-the-art methods on time series forecasting problem in the area of international relation forecasting, (ii) improving forecasting performance through the use of multimodal knowledge.