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genpark-knowledge-graph-entity-relation-triplet-extractor-skill

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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to build and query structured semantic graphs, extracting, traversing and multi-hop reasoning over entity-relation-object triples while producing Cypher and JSON-LD graph output. It also combines BM25 lexical and dense vector retrieval with rank fusion, semantic caching and context reordering for grounded agentic memory.
      7
      MIT
    • F
      license
      C
      quality
      D
      maintenance
      Enables enterprise document retrieval using graph-based reasoning and knowledge graphs. Allows agents to search and extract information from scattered documents through structured entity and relationship extraction.
      1
      2
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    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables storage and retrieval of knowledge in a graph database format, allowing users to create, update, search, and delete entities and relationships in a Neo4j-powered knowledge graph through natural language.
      5
      -
    • F
      license
      Not graded
      quality
      D
      maintenance
      Transforms text or web content into structured knowledge graphs using local AI models with MCP integration for persistent storage in Neo4j and Qdrant.
      4
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    • F
      license
      Not graded
      quality
      C
      maintenance
      Transforms code repositories and development documentation into a queryable Neo4j knowledge graph, enabling AI assistants to perform intelligent code analysis, dependency mapping, impact assessment, and automated documentation generation across 15+ programming languages.
      7
      -