KGCompiler: Deep Learning Compilation Optimization for Knowledge Graph Complex Logical Query Answering
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917944693358592 |
|---|---|
| author | Lin, Hongyu Luo, Haoran Cao, Hanghang Liu, Yang Gao, Shihao Yao, Kaichun Zhang, Libo Xing, Mingjie Wu, Yanjun |
| author_facet | Lin, Hongyu Luo, Haoran Cao, Hanghang Liu, Yang Gao, Shihao Yao, Kaichun Zhang, Libo Xing, Mingjie Wu, Yanjun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KGCompiler: Deep Learning Compilation Optimization for Knowledge Graph Complex Logical Query Answering Lin, Hongyu Luo, Haoran Cao, Hanghang Liu, Yang Gao, Shihao Yao, Kaichun Zhang, Libo Xing, Mingjie Wu, Yanjun Artificial Intelligence Software Engineering 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. |
| title | KGCompiler: Deep Learning Compilation Optimization for Knowledge Graph Complex Logical Query Answering |
| topic | Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2503.02172 |