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.
翻译:由于典型知识图的半结构性质和复杂关联性,对知识图(KG)的红心估计至关重要,但是由于典型知识图的半结构性质和复杂关联性,我们仍是一项具有挑战性的任务。在这项工作中,我们建议GNCE,这是一个利用知识图嵌入和图形神经网络(GNN)来准确预测聚合质询的基本要素的新办法。GNCE首先为KG中的所有实体创建具有内在意义的嵌入系统,然后将其纳入给定查询,然后由GNNE处理,以估计查询的主要性。我们建议的办法有可能大大改进查询的优化和相关的应用,这些应用以抽样、摘要和(机器)为基础,在估算准确性的同时,也缩短执行时间和减少参数。此外,我们表明GNCE可以对无形实体进行感应式概括,使之适合用于动态查询处理设想。我们提议的办法有可能大大改进基于对同质查询的精确基本性估计的查询的查询和(机器)应用。</s>