LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval

2025-10-06 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Expert, extended

Summary

LogosKG is a novel, hardware-aligned framework designed for scalable and interpretable multi-hop retrieval on large knowledge graphs (KGs). It addresses the challenges of efficiency, scalability, and interpretability in integrating KGs with large language models (LLMs) for structured, verifiable reasoning. LogosKG achieves this by building on symbolic KG formulations, executing traversal as hardware-efficient operations over decomposed subject, object, and relation representations, and integrating degree-aware partitioning, cross-graph routing, and on-demand caching to scale to billion-edge graphs. Experiments demonstrate substantial efficiency gains over CPU and GPU baselines without loss of retrieval fidelity, achieving perfect Jaccard similarity scores of 1.0. The framework also enables large-scale, evidence-grounded analysis of how KG topology shapes LLM diagnostic reasoning, particularly in biomedical contexts, and its source code and an online demo are publicly available.

Key takeaway

For NLP Engineers developing LLM applications requiring verifiable, multi-hop reasoning over large knowledge graphs, LogosKG offers a robust solution. Its hardware-optimized design and scalable retrieval mechanisms enable efficient access to deep KG structures, which can significantly enhance LLM accuracy and interpretability, especially in zero-shot or few-shot settings. You should consider integrating LogosKG to overcome computational barriers in high-hop retrieval and systematically analyze KG-LLM interactions.

Key insights

LogosKG enables scalable, interpretable multi-hop KG retrieval via hardware-aligned symbolic operations and partitioning for LLM integration.

Principles

• Decompose KGs into sparse incidence matrices for efficient hardware operations.
• Partition large KGs degree-awarely to manage memory and enable cross-graph routing.
• Utilize on-demand caching with LRU policy to reduce I/O overhead for large KGs.

Method

LogosKG decomposes KGs into subject, object, and relation matrices, performing multi-hop retrieval via sparse matrix multiplications. For large graphs, it partitions the KG, routes queries across subgraphs, and uses an LRU cache for on-demand subgraph loading.

In practice

• Apply LogosKG for evidence-grounded LLM analysis in high-stakes domains.
• Use LogosKG to explore LLM reasoning over deep KG structures.
• Integrate LogosKG as a backbone for multi-agent LLM frameworks.

Topics

• LogosKG Framework
• Multi-hop KG Retrieval
• Hardware-Aligned Optimization
• Graph Partitioning
• On-Demand Caching

Code references

• LARK-NLP-Lab/LogosKG
• rapidsai/cugraph

Best for: NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval — LogosKG offers scalable, interpretable k-hop retrieval for large knowledge graphs via hardware-optimized symbolic operations.
• GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion — GS-Quant aligns graph embeddings with LLM tokens using semantically coherent, hierarchically structured discrete codes.
• BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation — BLINKG provides a standardized benchmark to evaluate Large Language Models' effectiveness in mapping data schemes to ontology concepts for Knowledge Graph generation.
• Knowledge Graph Optimization — Slow knowledge graph queries are a subgraph matching problem requiring specific optimization techniques.
• GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning — GraphScout enables LLMs to autonomously explore knowledge graphs, internalizing reasoning through synthesized training data.
• Doubly Saturated Ramsey Graphs: A Case Study in Computer-Assisted Mathematical Discovery — Integrating SAT solving, LLMs, and formal verification accelerates mathematical discovery of complex graph structures.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.