knowledge-graph-creation

To turn unstructured text into a knowledge graph, the Skill extracts entities and directed relationships to produce subject-predicate-object triples. It generates Cypher CREATE statements, enabling direct import of the extracted graph data into Neo4j.

Yes, you can extract entities and map relationships from research papers by processing the text to identify named concepts, deduplicate aliases, and generate directed triples with source-sentence traceability for accurate knowledge mapping.

The best way to visualize text-extracted knowledge graphs is by generating Mermaid diagrams. The Skill outputs Mermaid visualizations alongside structured formats, allowing quick inspection of deduplicated entity nodes and their mapped relationships.

Yes, the knowledge graph extraction supports JSON-LD output. It transforms unstructured text into structured triples and exports them as JSON-LD, ensuring web interoperability and seamless integration with linked data ecosystems.

Duplicate entities are handled through an extraction and normalization process that deduplicates aliases and assigns consistent types and attributes. This ensures the generated directed triples contain clean, unified entity nodes without redundant mappings.

You can export text-mined knowledge graphs to Cypher for Neo4j, JSON-LD for web interoperability, CSV triples for tabular processing, and Mermaid diagrams for visualization. These formats support architecture docs, news analysis, and research papers.