LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs

Understand

Accurate cardinality estimates are a key ingredient to achieve optimal query plans.

• For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated predicates, and various types of queries involving multiple joins, render cardinality estimation a particularly challenging task.
• In this paper, we develop a framework, termed LMKG, that adopts deep learning approaches for effectively estimating the cardinality of queries over RDF graphs.
• We employ both supervised (i.e., deep neural networks) and unsupervised (i.e., autoregressive models) approaches that adapt to the subgraph patterns and produce more accurate cardinality estimates.