  Large-scale knowledge graphs are increasingly common in many domains. Their
large sizes often exceed the limits of systems storing the graphs in a
centralized data store, especially if placed in main memory. To overcome this,
large knowledge graphs need to be partitioned into multiple sub-graphs and
placed in nodes in a distributed system. But querying these fragmented
sub-graphs poses new challenges, such as increased communication costs, due to
distributed joins involving cut edges. To combat these problems, a good
partitioning should reduce the edge cuts while considering a given query
workload. However, a partitioned graph needs to be continually re-partitioned
to accommodate changes in the query workload and maintain a good average
processing time. In this paper, an adaptive partitioning method for large-scale
knowledge graphs is introduced, which adapts the partitioning in response to
changes in the query workload. Our evaluation demonstrates that the performance
of processing time for queries is improved after dynamically adapting the
partitioning of knowledge graph triples.
