To reduce hallucinations in large language models (LLMs), researchers are increasingly investigating reasoning methods that integrate LLMs with external knowledge graphs (KGs).Existing approaches either map an LLM-generated query graph onto the KG or let the LLM traverse the entire graph; the former is fragile because noisy query graphs derail retrieval, whereas the latter is inefficient due to entitylevel reasoning over large graphs.In order to tackle these problems, we propose SAFE (Schema-Driven Approximate Distance Join For Efficient Knowledge Graph Querying), a framework that leverages schema graphs for robust query graph generation and efficient KG retrieval.SAFE introduces two key ideas: (1) an Approximate Distance Join (ADJ) algorithm that refines LLM-generated pseudo query graphs by flexibly aligning them with the KG's structure; and (2) exploiting a compact schema graph to perform ADJ efficiently, reducing overhead and improving retrieval accuracy.Extensive experiments on WebQSP, CWQ and GrailQA demonstrate that SAFE outperforms state-of-the-art methods in both accuracy and efficiency, providing a robust and scalable solution to overcome the inherent limitations of LLM-based knowledge retrieval.

Recent efforts in learned cardinality estimation (CE) have substantially improved estimation accuracy and query plans inside query optimizers. However, achieving decent efficiency, scalability, and the support of a wide range of queries at the same time, has remained questionable. Rather than falling back to traditional approaches to trade off one criterion with another, we present a new learned approach that achieves all these. Our method, called ASM, harmonizes autoregressive models for per-table statistics estimation, sampling for merging these statistics for join queries, and multi-dimensional statistics merging that extends the sampling for estimating thousands of sub-queries, without assuming independence between join keys. Extensive experiments show that ASM significantly improves query plans under a similar or smaller overhead than the previous learned methods and supports a wider range of queries.