Knowledge graphs have broad applications across business, biomedical fields, and industrial digital twins, and this study focuses on their use in modeling household actions. Such graphs are particularly valuable for controlling household robots and interpreting video footage of household tasks. However, video-derived information is often incomplete, making knowledge graph completion crucial to enhance situational awareness. The paper investigates the challenges of applying link prediction methods to situational knowledge graphs representing household actions. It reveals that these graphs possess distinct features that cause many existing link prediction algorithms to underperform or fail to surpass simple baseline methods. This indicates a gap in current methodologies when dealing with the specific nature of situational graphs. The findings highlight the need for specialized approaches tailored to the characteristics of household action graphs. Improving knowledge graph completion in this domain could lead to better robot control and more accurate video analysis. Future work may involve developing novel algorithms or adapting existing ones to better handle the peculiarities of situational knowledge graphs. This research underscores the importance of context-aware methods in knowledge graph completion for practical applications.
👉 Pročitaj original: arXiv AI Papers
