Can an LLM Knowledge Graph Keep Two Unrelated Domains Apart?

2026-06-22 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, medium

Summary

A small-scale experiment using the "myKG" LLM-powered knowledge graph extractor investigated whether large language models hallucinate connections between unrelated domains when processing mixed document inputs. The test corpus comprised five Markdown files: one detailing the Star Wars trilogy and four describing a fictional IT organization (Acme Corp), ensuring zero real-world relationships between them. Using Google's Gemma 4 31B-it model, "myKG" processed these documents under three chunking strategies ("per_file", "concat", "batch_chunks") that varied how aggressively document chunks were mixed during entity extraction. Across all runs, the pipeline successfully separated the domains, generating zero cross-domain edges out of 245 total edges. This robust separation is attributed to "myKG"'s typed schema, the LLM's inherent semantic understanding, and co-occurrence-based post-processing steps, which collectively act as structural guardrails against hallucination.

Key takeaway

For AI Engineers building knowledge graphs from diverse, multi-domain document collections, you can confidently use LLM-powered extraction pipelines like "myKG" without extensive pre-sorting. This research indicates that robust pipelines, leveraging typed schemas and careful post-processing, effectively prevent hallucinated cross-domain connections. You should consider testing at production scale with partially overlapping domains to validate this separation for your specific use cases.

Key insights

The "myKG" pipeline effectively prevents LLM hallucination of cross-domain relationships in knowledge graph extraction through a multi-layered approach.

Principles

• Typed schemas constrain possible relationships.
• LLMs can scope extraction contextually.
• Multi-layered guardrails enhance robustness.

Method

"myKG" uses a two-pass pipeline: Pass 1 induces an RDFS/OWL schema from the corpus, then Pass 2 extracts typed entities and relationships against that schema, with confidence scores.

In practice

• Use "myKG" for heterogeneous document KGs.
• Consider "--base-schema" for ontology control.
• Review schema with "--review" before extraction.

Topics

• Knowledge Graph Extraction
• LLM Hallucination
• myKG Pipeline
• Domain Separation
• RDFS/OWL Ontology

Code references

• SenolIsci/mykg

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.