ai-knowledge-graph: The Intelligent Engine Transforming Text into Structured Knowledge

What is ai-knowledge-graph?

ai-knowledge-graph is an open-source project developed by Robert McDermott that aims to convert unstructured text—such as articles, reports, and books—into structured knowledge graphs. The system leverages large language models (LLMs) to extract subject-predicate-object (SPO) triples from text and visualizes the relationships between entities, helping users intuitively understand and analyze information.

Key Features

1. Text Segmentation:
   Automatically splits large documents into manageable chunks to fit within the context window of LLMs.

2. Knowledge Extraction:
   Uses LLMs to extract subject-predicate-object triples from each text chunk, identifying entities and their relationships.

3. Entity Normalization:
   Ensures consistent naming of the same entities across the entire document to reduce ambiguity.

4. Relationship Inference:
   Infers implicit relationships not explicitly mentioned in the text to connect fragmented parts of the graph.

5. Interactive Visualization:
   Generates knowledge graphs in HTML format with features such as zooming, dragging, and community detection for enhanced user interaction.

6. Multi-LLM Support:
   Compatible with various OpenAI-compatible API endpoints, including Ollama, LM Studio, OpenAI, vLLM, LiteLLM, offering flexible model choices.

Technical Principles

1. Text Segmentation and Processing:
   The input text is split into multiple chunks, each containing about 200 words with a 20-word overlap, to accommodate LLM context limits.

2. SPO Triple Extraction:
   Each text chunk is processed by an LLM to extract subject-predicate-object triples, forming the initial knowledge graph.

3. Entity Normalization:
   Recognized entities are standardized using LLM-based entity alignment to resolve inconsistent naming across text chunks.

4. Relationship Inference:
   Applies transitive closure and lexical similarity rules to infer relationships not explicitly stated, reducing graph fragmentation.

5. Graph Visualization:
   Uses the PyVis library to generate interactive HTML graphs supporting zoom, drag, and community detection, improving comprehension of knowledge structures.

Project Link

• GitHub Repository:
  https://github.com/robert-mcdermott/ai-knowledge-graph

Application Scenarios

1. Academic Research:
   Rapidly build knowledge graphs in research domains to assist literature reviews and research direction analysis.

2. Enterprise Knowledge Management:
   Integrate internal documents to construct knowledge graphs that enhance information retrieval and decision-making efficiency.

3. Education and Training:
   Transform educational materials into knowledge graphs to support teaching and learning.

4. News Analysis:
   Extract key information from news reports and build event relation graphs to assist public opinion analysis.

5. Legal Field:
   Analyze legal documents and construct case relation graphs to support legal research and case analysis.