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When evolving datasets are used to generate a knowledge graph, it is usually challenging to keep the graph synchronized in a timely manner when changes occur in the source data. Current approaches fully regenerate a knowledge graph in such cases, which may be time consuming depending on the data type, size, and update frequency. We propose a continuous knowledge graph generation approach that can be applied on different types of data sources. We describe continuously updating knowledge graphdblp:conf/esws/AsscheORC22 fatcat:szsvu6grtjfxtjfblgtm7cpjx4