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How to Query a Knowledge Graph Using an Ontology

  • Why this server?

    This server is designed for managing quantitative research knowledge graphs, enabling structured representation of research projects, datasets, variables, hypotheses, statistical tests, models, and results. It directly supports the user's need to query a knowledge graph.

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    Provides tools for managing quantitative research knowledge graphs, enabling structured representation of research projects, datasets, variables, hypotheses, statistical tests, models, and results.
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    TypeScript
    • Apple
    • Linux
  • Why this server?

    This server provides similar functionality to the Quantitative Researcher server, but focuses on qualitative research, representing projects, participants, interviews, observations, codes, themes, and findings. It caters to managing a knowledge graph but in the qualitative research domain.

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    Provides tools for managing qualitative research knowledge graphs, enabling structured representation of research projects, participants, interviews, observations, codes, themes, and findings.
    TypeScript
    • Apple
    • Linux
  • Why this server?

    This server offers a scalable, high-performance knowledge graph memory system with semantic search, temporal awareness, and advanced relation management, making it well-suited for querying complex knowledge graphs.

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    Scalable, high-performance knowledge graph memory system with semantic search, temporal awareness, and advanced relation management.
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    83
    40
    TypeScript
    MIT License
  • Why this server?

    This server provides persistent memory using a local knowledge graph, which allows AI to remember information across chats. This is helpful for maintaining context when querying knowledge graphs.

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    A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.
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    9,068
    37,968
    JavaScript
    MIT License
  • Why this server?

    This server enables storage and retrieval of knowledge in a graph database format (Neo4j), supporting creation, updating, search, and deletion of entities and relationships in a structured way.

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    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.
    Python
    • Linux
  • Why this server?

    A Model Context Protocol server enabling LLMs to interact with NebulaGraph database for graph exploration, supporting schema understanding, queries, and graph algorithms, useful for querying based on the graph structure.

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    A Model Context Protocol server implementation that enables LLMs to interact with NebulaGraph database for graph exploration, supporting schema understanding, queries, and graph algorithms.
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    Python
    Apache 2.0
  • Why this server?

    Enables natural language interaction with Apache AGE graph databases, allowing users to query, visualize and manipulate graph data in PostgreSQL through Claude AI, useful for ontology-driven queries.

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    An MCP server that provides natural language interaction with Apache AGE graph databases, allowing users to query, visualize and manipulate graph data in PostgreSQL through Claude AI.
    1
    Python
    MIT License
    • Apple
  • Why this server?

    A universal interface that enables AI Agents to communicate with Hologres databases, allowing them to retrieve database metadata and execute SQL operations which could be used as underlying data structure of the knowledge graph.

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    A universal interface that enables AI Agents to communicate with Hologres databases, allowing them to retrieve database metadata and execute SQL operations.
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    17
    Python
    Apache 2.0
  • Why this server?

    Provides vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage. This can be useful to build the required knowledge graph.

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    A
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    A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.
    17
    Python
    MIT License
    • Apple
    • Linux
  • Why this server?

    Manages AI conversation context and personal knowledge bases through the Model Context Protocol (MCP), providing tools for user data, conversation content, and knowledge management which can be adapted as a knowledge graph.

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    Manages AI conversation context and personal knowledge bases through the Model Context Protocol (MCP), providing tools for user data, conversation content, and knowledge management.
    1
    TypeScript