A Framework for Constructing and Augmenting Knowledge Graphs using Virtual Space: Towards Analysis of Daily Activities

Daily living studies typically necessitate the use of a physical environment such as cameras, sensor networks, or experimental space. Moreover, it is difficult to collect data by flexibly changing the conditions. In the future, data from a physical space that can acquire real data and a virtual space that can easily change conditions and perform many experiments will need to be combined for analysis of daily life. This study proposes a framework for constructing and augmenting knowledge graphs (KGs) based on simulation results of daily living activities, using virtual space to enable various analyses of daily living activities. First, we design an ontology to represent virtual space activities and situational changes. Then, we construct KGs for the everyday living simulation. Second, we propose a method for KG augmentation that employs Markov chain to combine multiple activities KGs. Furthermore, we present several use cases using SPARQL queries and a KG embedding method. We also discuss the KG generation method, the proposed ontology, and the potential for expansion.

Automated summary

The study proposes a framework for constructing and augmenting knowledge graphs based on simulation results of daily living activities, using virtual space to enable various analyses of daily living activities. It involves designing an ontology to represent virtual space activities and situational changes, constructing KGs for everyday living simulation, proposing a method for KG augmentation using Markov chain, presenting use cases with SPARQL queries and KG embedding method, and discussing the KG generation method, ontology, and potential for expansion.