Research on knowledge drift based on interaction matching

Drift detection is a fundamentally challenging task in data-driven decision-making; however, traditional distribution-based methods for data drift detection fail to adequately capture the characteristics of knowledge-centric data-driven decision-making. Thus, exploring knowledge-based data drift detection methods has extensive practical value. Herein, we consider If-Then rules as the knowledge representation framework, focusing on changes in the quantity and quality of knowledge and utilizing the interaction matching of rules as a measure of knowledge differentiation. First, we propose the concept of forward and reverse rule (trust) drift and introduce a knowledge drift detection model based on interaction matching (named rule knowledge drift detection [RKDD]). The basic features of RKDD are analyzed and discussed. Second, using statistical theory, we discuss the convergence characteristics of rule knowledge and propose a knowledge drift detection model that utilizes interaction matching within the framework of sampling (named sample-rule knowledge drift detection [S-RKDD]). Finally, we compare and analyze the effectiveness of RKDD using three University of California Irvine (UCI) datasets. Theoretical analysis and experimental results demonstrate that RKDD possesses good structural characteristics and interpretability, enabling the integration of decision awareness into the decision-making process through simple parameter adjustments, thereby enriching the existing data drift detection theory to a certain extent.