Extract entities and relationships from text and compress knowledge graphs while preserving query capabilities.
npx skills add https://github.com/Zpankz/mcp-skillset --skill graph-zpankz
Please help me install this Agent Skill. Skill: graph Source: https://github.com/Zpankz/mcp-skillset/tree/main/graph Command: npx skills add https://github.com/Zpankz/mcp-skillset --skill graph-zpankz
Knowledge graphs often grow large and unwieldy, making extraction, maintenance, and query answering expensive. This Skill offers end-to-end support for entity and relationship extraction, ontology-driven schema application, and compression techniques that preserve query semantics while dramatically reducing size. It enables domain-specific modeling and multi-scale representations via metagraphs and category-theoretic quotients.
Run a sample compression workflow on a knowledge graph to see end-to-end extraction, validation, and query-preserving compression in action.
High-intent search queries and answers about installing and using this skill.
Knowledge graph compression uses automorphism-based reduction and k-bisimulation summarization to shrink graph size while preserving query semantics. Categorical quotient constructions maintain formal guarantees for query answering on the compressed structure.
K-bisimulation summarization groups structurally equivalent nodes in a knowledge graph to produce a smaller quotient graph. This technique retains query preservation capabilities and topology metrics while dramatically reducing the overall graph scale.
Structured entity extraction maps relationships across domain schemas and applies confidence scoring with provenance tracking. This process builds ontology-driven knowledge graphs by linking extracted text entities to categorical ontology definitions.
Metagraph hierarchical modeling is needed when managing multi-scale representations in large knowledge graphs. It enables domain-specific schema application and categorical quotient construction to maintain topology metrics across different abstraction layers.
Graph compression operates directly on domain-specific schemas while maintaining provenance and confidence scores for extracted entities. Quality validation and query-preservation verification ensure the compressed graph retains structural fidelity and domain ontology rules.
Automorphism-based compression effectiveness depends on the structural redundancy within the knowledge graph; highly irregular graphs yield minimal size reduction. Quotient constructions and skeleton-based representations must still pass query-preservation verification to ensure valid analytics.
prathamchopra001Propose entity relationships with confidence scores from contextual evidence.
ZpankzOrchestrate InfraNodus workflows for knowledge graphs and Obsidian documentation.
0bserver07Prove ring and ideal properties with machine-checked Lean 4 proofs.
oakossDesign ontologies and select graph databases for knowledge graph implementation.
