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Accurate cardinality estimates are a key ingredient to achieve optimal queryplans. For RDF engines, specifically under common knowledge graph processingworkloads, the lack of schema, correlated predicates, and various types ofqueries involving multiple joins, render cardinality estimation a particularlychallenging task. In this paper, we develop a framework, termed LMKG, thatadopts deep learning approaches for effectively estimating the cardinality ofqueries over RDF graphs. We employ both supervised (i.e., deep neural networks)and unsupervised (i.e., autoregressive models) approaches that adapt to thesubgraph patterns and produce more accurate cardinality estimates. To feed theunderlying data to the models, we put forward a novel encoding that representsthe queries as subgraph patterns. Through extensive experiments on bothreal-world and synthetic datasets, we evaluate our models and show that theyoverall outperform the state-of-the-art approaches in terms of accuracy andexecution time.

LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs