INDIAN MEDICINE MCP SERVER

🚀 Medical Knowledge Graph Question Answering System

The Medical Knowledge Graph Question Answering System is an intelligent Q&A platform based on a knowledge graph, aiming to provide users with professional and accurate medical and health information. The system integrates rich medical data and advanced natural language processing technologies, enabling it to understand and answer complex medical-related questions.

✨ Features

1. System Introduction

The Medical Knowledge Graph Question Answering System is an intelligent Q&A platform based on a knowledge graph. It aims to provide users with professional and accurate medical and health information. The system integrates rich medical data and advanced natural language processing technologies, enabling it to understand and answer complex medical-related questions.

2. Core Functional Modules

a) Knowledge Base Construction

• Medical Ontology: Contains core medical concepts such as diseases, symptoms, drugs, and examination items.
• Relationship Network: Describes the associations between various medical entities, such as "Hypertension may lead to heart disease" and "Aspirin is used to treat headaches".
• Q&A Rules: Predefined medical question answering templates and reasoning logics.

b) Q&A Engine

• Semantic Parsing: Transforms user input questions into a computer-understandable form.
• Knowledge Retrieval: Searches for relevant knowledge nodes in the knowledge graph.
• Reasoning Calculation: Infers through associated paths to obtain the final answer.
• Result Optimization: Ranks multiple possible answers based on evidence strength and credibility.

c) User Interface

• Provides Q&A functionality with natural language input.
• Supports context conversation history.
• Displays the reliable sources of results.
• Presents the knowledge reasoning process.

3. Core Algorithms

a) Knowledge Graph Construction Algorithms

• Information Extraction: Extracts entities and relationships from medical literature and drug instructions.
• Semantic Disambiguation: Resolves the problem of homonyms.
• Knowledge Fusion: Integrates multi-source data and eliminates conflicts.

b) Q&A Reasoning Algorithms

• Shortest Path Rule: Prioritizes the shortest knowledge association path.
• Evidence Credibility Assessment: Considers the authority and timeliness of knowledge sources comprehensively.
• Result Confidence Calculation: Determines the reliability of the final answer through a weighted scoring mechanism.

4. System Architecture

a) Technology Selection

• Storage Technology: Uses Neo4j for graph database storage.
• Computing Framework: Based on Spark for large-scale data processing.
• Natural Language Processing: Combines spaCy and HanLP.
• Reasoning Engine: Uses the logical reasoning library Prolog for rule reasoning.

b) Module Division

• Knowledge Extraction Module
• Knowledge Storage Module
• Q&A Service Module
• Reasoning Calculation Module
• User Interaction Module

🔧 Technical Details

1. Basic Data Tables

a) Medical Entity Table

b) Relationship Table

2. Knowledge Graph File

The knowledge graph is stored in JSON-L format, with each document representing a knowledge point:

{
  "id": "med-001",
  "type": "Disease",
  "name": "Hypertension",
  "synonyms": ["high blood pressure"],
  "description": "A common chronic disease characterized by persistent high blood pressure.",
  "relations": [
    { "target": "Heart Disease", "type": "lead to" },
    { "target": "Arteriosclerosis", "type": "associated with" }
  ]
}

Performance Optimization

1. Query Optimization

• Index Optimization: Builds composite indexes on the entity table and the relationship table.
• Caching Mechanism: Introduces Redis to cache hot data.
• Paged Loading: Limits the number of knowledge nodes retrieved in each query.

2. Rule Optimization

• Path Length Control: Sets the maximum number of reasoning steps.
• Weight Threshold Filtering: Ignores knowledge associations with low weights.
• Trusted Source Priority: Prioritizes information published by authoritative institutions.

🤝 How to Contribute

1. Participation Methods

Community developers are welcome to participate in the project:

• Submit code fixes.
• Contribute new functional modules.
• Report problem defects.
• Review others' code.

2. Development Guide

Please refer to the project documentation:

• Development Environment Setup
• Interface Specification
• Code Style Guide

3. List of Contributors

Thanks to the following contributors:

• Zhang San (Development of functional modules)
• Li Si (Writing of test cases)
• Wang Wu (Documentation organization)

📄 License

This project uses the MIT open-source license, allowing free use, modification, and distribution.

Please see the license copy at: LICENSE

📞 Contact Information

For feedback or cooperation, please contact:

• Email: support@medkg.com
• Phone: +86-123-4567890

The above is the complete documentation for the Medical Knowledge Graph Question Answering System. We welcome all developers to actively participate in the project co-construction.