We are pleased to share that the paper “A Comprehensive Survey of Stream Reasoning and its Integration with Knowledge Graphs” by Tataroğlu Özbulak, G. A, Shrestha, Y. R., & Calbimonte, J. P. has be accepted and published in the journal Knowledge and Information Systems (KAIS)!
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.
