Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs
Hardware datasheets contain engineering specifications essential for component selection, system integration, and hardware compatibility verification. However, manual inspection is tedious and error-prone because datasheets are lengthy and heterogeneous, with dense information fragmented across…
This article presents a framework for automating the construction of hierarchical hardware knowledge graphs from engineering datasheets using large language models (LLMs). The system addresses challenges in manual data extraction by improving accuracy and coverage through staged processing.
What happened
The article introduces a staged LLM-based system for converting hardware datasheets into structured knowledge graphs. The framework processes documents in chunks, maintains traceable intermediate representations, and uses validated instructions for graph updates. Evaluation on 20 hardware datasheets showed significant improvements in accuracy and coverage compared to baseline methods.
Why it matters
The system provides more reliable engineering information for hardware design and verification, reducing risks of overlooked constraints and incorrect decisions in complex system integration projects.
- Staged processing improves LLM performance on complex documents
- Traceable intermediate representations enhance reliability
- The framework outperforms one-shot methods in both faithfulness and coverage
- Knowledge graphs provide structured, source-grounded hardware data for AI applications
The bigger picture
The framework enables more efficient and accurate hardware specification management, potentially improving downstream tasks like system compatibility verification and component selection in engineering workflows.
The article demonstrates a novel approach to automating hardware datasheet analysis through AI-enhanced knowledge graph construction, offering significant improvements in accuracy and comprehensiveness for engineering applications.
Technical details
- Chunk-based property extraction
- Hierarchical routing of extracted information
- Iterative refinement with validated instructions
- Graph updates through deterministic local execution
- Property faithfulness metrics
- Inter-graph completeness measurements
FAQ
What problem does this framework solve?
It automates the manual and error-prone process of extracting and organizing hardware specifications from complex datasheets.
How does the framework improve upon existing methods?
By using staged processing, hierarchical routing, and iterative refinement, it achieves higher property faithfulness (98.8%) and coverage (83.6%) compared to one-shot approaches.
What are the key technical contributions?
A pipeline for LLM-assisted knowledge graph construction with traceable intermediate representations and metrics for evaluating faithfulness and completeness.
What types of hardware data can this system process?
It was evaluated on 20 diverse hardware datasheets spanning multiple component types and document lengths.
How does the system ensure data reliability?
By preserving source provenance, using validated instructions for graph updates, and maintaining deterministic local execution throughout the process.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.AI on Oct 12, 2026.
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