A comprehensive survey of stream reasoning and its integration with knowledge graphs

Abstract The rapid expansion of decentralized, complex streaming data across diverse domains such as the Internet of Things, healthcare, and smart cities presents significant technical challenges. These challenges–data heterogeneity (integration of diverse formats and sources), dynamicity (handling real-time data evolution), and high-volume throughput (efficient processing of large, rapidly arriving data)–are the central focus of this study and are examined in depth. To address these critical issues necessitates advanced methods capable of seamless integration, effective real-time reasoning, and continuous learning from heterogeneous streaming data, thus enhancing real-time decision-making capabilities. This study provides an extensive review of existing research at the intersection of streaming data, machine learning, and reasoning. The literature review categorizes Stream Reasoning approaches into three key groups: Streaming Machine Learning, Streaming Linked Data, and Streaming Knowledge Graphs. Each category is critically examined in terms of strengths, limitations, ongoing challenges, and future opportunities identified in recent studies. Additionally, potential integrative solutions that leverage Knowledge Graph structures and advanced Stream Reasoning techniques are highlighted, illustrating how state-of-the-art modeling methods can effectively address Stream Reasoning related challenges. The analysis concludes that combining Knowledge Graph and Machine Learning approaches significantly enhances the capability to manage and overcome complex Stream Reasoning challenges.