针对家庭任务场景图补全，提出适配其特性的新方法
Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks
- 针对家庭任务图结构设计专用补全机制
- 在真实数据上显著优于主流链接预测算法
- 适合机器人控制与视频理解场景的开发者
知识图谱广泛应用于商业、生物医学及工业4.0数字孪生等领域。本文研究描述家庭动作的知识图谱，有助于控制家用机器人和分析视频内容。视频信息通常不完整，因此补全知识图谱以增强情境理解至关重要。本文发现，尽管属于标准链接预测问题，但情境知识图谱具有特殊属性，使许多现有链接预测算法不适用，甚至无法超越简单基线。
原文摘要 · Abstract (English)
Knowledge Graphs are used for various purposes, including business applications, biomedical analyses, or digital twins in industry 4.0. In this paper, we investigate knowledge graphs describing household actions, which are beneficial for controlling household robots and analyzing video footage. In the latter case, the information extracted from videos is notoriously incomplete, and completing the knowledge graph for enhancing the situational picture is essential. In this paper, we show that, while a standard link prediction problem, situational knowledge graphs have special characteristics that render many link prediction algorithms not fit for the job, and unable to outperform even simple baselines.
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