  Cardinality Estimation over Knowledge Graphs (KG) is crucial for query
optimization, yet remains a challenging task due to the semi-structured nature
and complex correlations of typical Knowledge Graphs. In this work, we propose
GNCE, a novel approach that leverages knowledge graph embeddings and Graph
Neural Networks (GNN) to accurately predict the cardinality of conjunctive
queries. GNCE first creates semantically meaningful embeddings for all entities
in the KG, which are then integrated into the given query, which is processed
by a GNN to estimate the cardinality of the query. We evaluate GNCE on several
KGs in terms of q-Error and demonstrate that it outperforms state-of-the-art
approaches based on sampling, summaries, and (machine) learning in terms of
estimation accuracy while also having lower execution time and less parameters.
Additionally, we show that GNCE can inductively generalise to unseen entities,
making it suitable for use in dynamic query processing scenarios. Our proposed
approach has the potential to significantly improve query optimization and
related applications that rely on accurate cardinality estimates of conjunctive
queries.
