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 graph
versions represented as a Linked Data Events Stream,
and use an RML processor for RDF generation. In this paper,
we present our approach and demonstrate it on different types of data
such as bike-sharing, public transport timetables, and weather data.
By describing entities with unique, immutable, and reproducible
IRIs, we were able to identify changes in the original data collection,
reducing the number of materialized triples and generation time.
Our use-cases show the importance of mechanisms to derive unique and
stable IRI strategies of data source updates, to enable efficient
knowledge graph generation pipelines. In the future, we will extend our
approach to handle deletions in data collections, and conduct an extensive
performance evaluation.