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Knowledge Network Extraction and Query System: Design and Implementation Document

1. System Overview

The Knowledge Network Extraction and Query System is designed to process textbooks and other academic materials, extract key information, and create a queryable knowledge graph. The system aims to provide an intuitive way for users to navigate and search through complex academic content.

2. System Architecture

The system is composed of five core modules:

  1. Data Ingestion Module
  2. Graph Construction Module
  3. Text Reference Integration Module
  4. Query Interface Module
  5. User Interaction Module

2.1 High-Level Architecture Diagram

[Data Source (Textbooks, Papers)] 
            |
            v
[Data Ingestion Module]
            |
            v
[Graph Construction Module] <--> [Text Reference Integration Module]
            |
            v
[Query Interface Module]
            |
            v
[User Interaction Module]
            |
            v
[End User Interface]

3. Detailed Module Descriptions

3.1 Data Ingestion Module

Purpose:

Process raw text data and prepare it for graph construction.

Components:

  • Text Segmentation Engine
  • Named Entity Recognition (NER) System
  • Embedding Generator

Key Functions:

  • segment_text(text: str) -> List[TextSegment]
  • extract_entities(segment: TextSegment) -> List[Entity]
  • generate_embedding(text: str) -> np.array

Technologies:

  • Text Segmentation: Custom rule-based system or NLTK
  • NER: spaCy or Stanford NER
  • Embedding: Sentence-BERT or Word2Vec

3.2 Graph Construction Module

Purpose:

Build the knowledge graph based on processed data.

Components:

  • Hierarchical Node Creator
  • Entity Node Integrator
  • Relationship Establisher

Key Functions:

  • create_hierarchical_nodes(segments: List[TextSegment]) -> List[Node]
  • create_entity_nodes(entities: List[Entity]) -> List[Node]
  • establish_relationships(nodes: List[Node]) -> List[Relationship]

Technologies:

  • Graph Database: Neo4j
  • Graph Processing: NetworkX

3.3 Text Reference Integration Module

Purpose:

Link graph nodes to original text for quick reference.

Components:

  • Metadata Associator
  • Text Snippet Store

Key Functions:

  • associate_metadata(node: Node, metadata: Dict) -> Node
  • store_text_snippet(node: Node, text: str) -> str

Technologies:

  • Metadata Storage: JSON or XML
  • Text Storage: ElasticSearch or PostgreSQL

3.4 Query Interface Module

Purpose:

Enable various types of searches on the knowledge graph.

Components:

  • Structural Search Engine
  • Entity-Based Search Engine
  • Similarity Search Engine

Key Functions:

  • search_by_structure(query: str) -> List[Node]
  • search_by_entity(entity: str) -> List[Node]
  • search_by_similarity(text: str, threshold: float) -> List[Node]

Technologies:

  • Graph Querying: Cypher (Neo4j query language)
  • Similarity Search: Faiss or Annoy

3.5 User Interaction Module

Purpose:

Provide a user-friendly interface for interacting with the knowledge graph.

Components:

  • Graph Visualizer
  • Text Retrieval Interface
  • Search Filter System

Key Functions:

  • visualize_graph(nodes: List[Node], relationships: List[Relationship]) -> Visualization
  • retrieve_text(node: Node) -> str
  • apply_filters(results: List[Node], filters: Dict) -> List[Node]

Technologies:

  • Visualization: D3.js or Cytoscape.js
  • Frontend: React or Vue.js
  • Backend API: Flask or FastAPI

4. Data Flow

  1. Raw text is input into the Data Ingestion Module.
  2. Processed data (segments, entities, embeddings) is passed to the Graph Construction Module.
  3. The Graph Construction Module builds the graph and interacts with the Text Reference Integration Module to associate original text.
  4. The Query Interface Module interacts with the constructed graph to perform searches.
  5. The User Interaction Module presents results and allows for graph exploration.

5. Implementation Plan

Phase 1: Data Ingestion and Preparation (Weeks 1-4)

  • Week 1-2: Implement Text Segmentation
  • Week 3: Implement NER Integration
  • Week 4: Implement Embedding Generation

Phase 2: Graph Construction (Weeks 5-8)

  • Week 5-6: Implement Hierarchical Node Creation
  • Week 7: Implement Entity and Relationship Mapping
  • Week 8: Implement Text Reference Mapping

Phase 3: Query Interface Development (Weeks 9-12)

  • Week 9-10: Implement Basic Query Functionality
  • Week 11-12: Implement Advanced Query Options

Phase 4: User Interaction and Visualization (Weeks 13-16)

  • Week 13-14: Develop Graph Visualization Tools
  • Week 15-16: Implement Text Access and Navigation

6. Testing Strategy

  • Unit Testing: For individual components and functions
  • Integration Testing: For module interactions
  • System Testing: End-to-end testing of the entire system
  • User Acceptance Testing: Involve end-users to gather feedback

7. Deployment Strategy

  • Containerization: Docker for easy deployment and scaling
  • Cloud Hosting: AWS or Google Cloud Platform
  • Continuous Integration/Continuous Deployment (CI/CD): Jenkins or GitLab CI

8. Maintenance and Updates

  • Regular performance monitoring and optimization
  • Scheduled reviews for potential new features or improvements
  • Ongoing updates to NLP models and embedding techniques

9. Conclusion

This design and implementation document provides a roadmap for developing the Knowledge Network Extraction and Query System. By following this modular and phased approach, we can create a robust, scalable, and user-friendly system for navigating complex academic content.