LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval

2026-04-20

AI Summary

Existing knowledge graph multi-hop retrieval systems struggle to simultaneously achieve efficiency, scalability, and interpretability. This work proposes a hardware-aligned symbolic retrieval framework that reformulates multi-hop reasoning as efficient hardware operations over decomposed representations of subjects, predicates, and objects. By integrating degree-aware graph partitioning, cross-partition routing, and on-demand caching, the approach enables highly efficient retrieval at billion-edge scale. It is the first method to deliver interpretable, scalable, and hardware-efficient multi-hop retrieval on both CPUs and GPUs, significantly accelerating inference while preserving high fidelity. The framework has been successfully deployed in biomedical domains for collaborative reasoning between knowledge graphs and large language models.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Hardware-aware ML
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Knowledge graphs (KGs) are increasingly integrated with large language models (LLMs) to provide structured, verifiable reasoning. A core operation in this integration is multi-hop retrieval, yet existing systems struggle to balance efficiency, scalability, and interpretability. We introduce LogosKG, a novel, hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs by building on symbolic KG formulations and executing traversal as hardware-efficient operations over decomposed subject, object, and relation representations. To scale to billion-edge graphs, LogosKG integrates degree-aware partitioning, cross-graph routing, and on-demand caching. Experiments show substantial efficiency gains over CPU and GPU baselines without loss of retrieval fidelity. With proven performance in KG retrieval, a downstream two-round KG-LLM interaction demonstrates how LogosKG enables large-scale, evidence-grounded analysis of how KG topology, such as hop distribution and connectivity, shapes the alignment between structured biomedical knowledge and LLM diagnostic reasoning, thereby opening the door for next-generation KG-LLM integration. The source code is publicly available at https://github.com/LARK-NLP-Lab/LogosKG, and an online demo is available at https://lark-nlp-lab-logoskg.hf.space/.

Problem

• knowledge graph retrieval
• multi-hop reasoning
• scalability
• interpretability
• hardware efficiency

Innovation

• hardware-optimized retrieval
• multi-hop knowledge graph traversal
• degree-aware partitioning
• KG-LLM integration
• interpretable symbolic reasoning