kg_text.txt

To create a knowledge graph that helps you find documents using natural language queries from a large collection of thousands of documents, you can follow these steps:

1. Document Processing:
- Convert all documents to a standard format (e.g., plain text)
- Split documents into smaller chunks or paragraphs

2. Entity and Relation Extraction:
- Use Named Entity Recognition (NER) to identify key entities in each document chunk
- Employ relation extraction techniques to identify relationships between entities

3. Knowledge Graph Construction:
- Create nodes for each unique entity
- Create edges between nodes based on extracted relationships
- Link document chunks to relevant entity nodes

4. Embedding Generation:
- Generate embeddings for entities, relationships, and document chunks using techniques like BERT or other language models

5. Graph Database Integration:
- Store the knowledge graph in a graph database (e.g., Neo4j, ArangoDB)
- Index the embeddings for efficient similarity search

6. Natural Language Query Processing:
- Implement a query processing pipeline that:
  a. Extracts key entities and concepts from the natural language query
  b. Generates an embedding for the query

7. Retrieval and Ranking:
- Use a combination of graph traversal and embedding similarity to find relevant documents:
  a. Identify nodes in the graph that match query entities
  b. Traverse the graph to find connected documents
  c. Use embedding similarity to rank results

8. Result Presentation:
- Return the most relevant document chunks or full documents based on the ranking

To implement this approach:

1. Use NLP libraries like spaCy or Stanford NLP for entity and relation extraction.

2. Employ a graph database like Neo4j with its built-in graph algorithms.

3. Utilize embedding models like BERT or sentence-transformers for generating embeddings.

4. Implement a vector similarity search using libraries like FAISS or Annoy for efficient retrieval.

5. Develop a custom ranking algorithm that combines graph-based relevance and embedding similarity.

This approach allows you to leverage both the structured information in the knowledge graph and the semantic understanding provided by embeddings. It enables natural language queries to find relevant documents even when they don't contain exact keyword matches, as the system can understand concepts and relationships[1][2].

Remember that building and maintaining such a system for thousands of documents requires significant computational resources and ongoing updates as new documents are added or existing ones are modified.

Citations:
[1] https://www.datastax.com/blog/how-knowledge-graph-rag-boosts-llm-results
[2] https://arxiv.org/html/2107.04771v2
[3] https://link.springer.com/chapter/10.1007/978-981-99-7649-2_21
[4] https://arxiv.org/abs/2305.12416
[5] https://neo4j.com/developer-blog/enhance-rag-knowledge-graph/
[6] https://github.com/AnjaneyaTripathi/knowledge_graph
[7] https://papers.academic-conferences.org/index.php/eckm/article/view/2876
[8] https://arxiv.org/html/2409.13252v1



reddit
|

https://www.reddit.com/r/LocalLLaMA/comments/18x3ms3/rag_over_knowledge_graphs/?rdt=65176