TL;DR
This paper introduces VirtualHome2KG, a framework for generating synthetic knowledge graphs of daily activities in virtual space to facilitate AI understanding of human behavior, overcoming data collection challenges.
Contribution
The study presents a novel framework for creating event-centric knowledge graphs from virtual space simulations, enhancing data availability for AI applications in daily activity understanding.
Findings
Effective generation of synthetic KGs from virtual activity data
Demonstrated applications include activity querying, embedding, and clustering
Potential for fall risk detection and other health-related analyses
Abstract
Artificial intelligence (AI) is expected to be embodied in software agents, robots, and cyber-physical systems that can understand the various contextual information of daily life in the home environment to support human behavior and decision making in various situations. Scene graph and knowledge graph (KG) construction technologies have attracted much attention for knowledge-based embodied question answering meeting this expectation. However, collecting and managing real data on daily activities under various experimental conditions in a physical space are quite costly, and developing AI that understands the intentions and contexts is difficult. In the future, data from both virtual spaces, where conditions can be easily modified, and physical spaces, where conditions are difficult to change, are expected to be combined to analyze daily living activities. However, studies on the KG…
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