KGCompiler: Deep Learning Compilation Optimization for Knowledge Graph Complex Logical Query Answering
This addresses scalability challenges for deploying CLQA systems in practical applications, offering a compiler-level optimization that is more general and scalable than algorithm-level approaches.
The paper tackles the problem of high reasoning time and memory usage in Complex Logical Query Answering (CLQA) over Knowledge Graphs by introducing KGCompiler, a deep learning compiler that accelerates CLQA algorithms by an average of 3.71x and reduces memory usage.
Complex Logical Query Answering (CLQA) involves intricate multi-hop logical reasoning over large-scale and potentially incomplete Knowledge Graphs (KGs). Although existing CLQA algorithms achieve high accuracy in answering such queries, their reasoning time and memory usage scale significantly with the number of First-Order Logic (FOL) operators involved, creating serious challenges for practical deployment. In addition, current research primarily focuses on algorithm-level optimizations for CLQA tasks, often overlooking compiler-level optimizations, which can offer greater generality and scalability. To address these limitations, we introduce a Knowledge Graph Compiler, namely KGCompiler, the first deep learning compiler specifically designed for CLQA tasks. By incorporating KG-specific optimizations proposed in this paper, KGCompiler enhances the reasoning performance of CLQA algorithms without requiring additional manual modifications to their implementations. At the same time, it significantly reduces memory usage. Extensive experiments demonstrate that KGCompiler accelerates CLQA algorithms by factors ranging from 1.04x to 8.26x, with an average speedup of 3.71x. We also provide an interface to enable hands-on experience with KGCompiler.