[This blog post is based on Jesse van Haaster‘s bachelor thesis Artificial Intelligence at VU]
Knowledge Graphs represent data as triples, connecting related data points. This form of representation is widely used for various applications, such as querying information and drawing inferences from data. For fine-tuning such applications, actual KGs are needed. However, in certain domains like medical records or smart home devices, creating large-scale public knowledge graphs is challenging due to privacy concerns. To address this, generating synthetic knowledge graph data that mimics the original while preserving privacy is highly beneficial.
Jesse’s thesis explored the feasibility of generating meaningful synthetic time series data for knowledge graphs. He specifically does this in the smart building / IoT domain, building on our previous work on IoT knowledge graphs, including OfficeGraph.
To this end, two existing generative adversarial networks (GANs), CTGAN and TimeGAN, are evaluated for their ability to produce synthetic data that retains key characteristics of the original OfficeGraph dataset. Jesse compared among other things the differences in distributions of values for key features, such as humidity, temperature and co2 levels, seen below.
The experiment results indicate that while both models capture some important features, neither is able to replicate all of the original data’s properties. Further research is needed to develop a solution that fully meets the requirements for generating meaningful synthetic knowledge graph data.
More details can be found in Jesse’s thesis (found below) and his Github repository https://github.com/JaManJesse/SyntheticKnowledgeGraphGeneration
