Docs2KG

A Human-LLM Collaborative Approach to Unified Knowledge Graph Construction from Heterogeneous Documents

Installation

We have published the package to PyPi: Docs2KG,

You can install it via:

pip install Docs2KG

python -m spacy download en_core_web_sm

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Setup and Tutorial

Detailed setup and tutorial can be found in the documentation.

You have two ways to run the package:

• import the package in the code, and hook it with your own code
• run the package in the command line

Command Line

# first setup the CONFIG_FILE environment variable to local one
export CONFIG_FILE=config.yml # or any other path for the configuration file
docs2kg # this command will tell you how to use the package

# we currently support the following commands
docs2kg process-document your_input_file --agent-name phi3.5 --agent-type ollama --project-id your_project_id
docs2kg batch-process your_input_dir --agent-name phi3.5 --agent-type ollama --project-id your_project_id
docs2kg list-formats # list all the supported formats

Usage: docs2kg [OPTIONS] COMMAND [ARGS]...

  Docs2KG - Document to Knowledge Graph conversion tool.

  Supports multiple document formats: PDF, DOCX, HTML, and EPUB.

Options:
  -c, --config PATH  Path to the configuration file (default: ./config.yml)
  --help             Show this message and exit.

Commands:
  batch-process     Process all supported documents in a directory.
  list-formats      List all supported document formats.
  neo4j             Load data to Neo4j database.
  process-document  Process a single document file.

Usage: docs2kg process-document [OPTIONS] FILE_PATH

  Process a single document file.

  FILE_PATH: Path to the document file (PDF, DOCX, HTML, or EPUB)

Options:
  -p, --project-id TEXT  Project ID for the knowledge graph construction
  -n, --agent-name TEXT  Name of the agent to use for NER extraction
  -t, --agent-type TEXT  Type of the agent to use for NER extraction
  --help                 Show this message and exit.

Usage: docs2kg neo4j [OPTIONS] PROJECT_ID

  Load data to Neo4j database.

Options:
  -m, --mode [import|export|load|docker_start|docker_stop]
                                  Mode of operation (import or export)
  -u, --neo4j-uri TEXT            URI for the Neo4j database
  -U, --neo4j-user TEXT           Username for the Neo4j database
  -P, --neo4j-password TEXT       Password for the Neo4j database
  -r, --reset_db                  Reset the database before loading data
  --help      

Motivation

To digest diverse unstructured documents into a unified knowledge graph, there are two main challenges:

• How to get the documents to be digitized?
  • With the dual-path data processing
    • For image based documents, like scanned PDF, images, etc., we can process them through the layout analysis and OCR, etc. Docling and MinerU are focusing on this part.
    • For native digital documents, like ebook, docx, html, etc., we can process them through the programming parser
  • It is promising that we will have a robust solution soon.
• How to construct a high-quality unified knowledge graph with less effort?

For now, a lot of tools are focusing on the first challenge, however, overlook the second challenge.

To construct a high-quality unified knowledge graph with less effort, we propose the Docs2KG.

• We adapt both bottom-up and top-down approaches to construct the unified knowledge graph and its ontology with the help of LLM.
• We organise the knowledge graph from three aspects:
  • MetaKG: the knowledge about all documents, like the author, the publication date, etc.
  • LayoutKG: the knowledge about the layout of the documents, like title, subtitle, section, etc.
  • SemanticKG: the knowledge about the content of the documents, like entities, relations, etc.
• We provide a human-LLM collaborative interface which allows human to review and enhance the generated knowledge graph.
  • An updated version of ontology, entity list, relation list will in return help the KG Construction LLM agent to generate better results in the next iteration.
  • The output of the knowledge graph can be used in downstream applications, like RAG, etc.
  • Link for the human-LLM collaborative interface: Docs2KG
  • After the annotation, metrics to evaluate the quality of automatic construction will be provided.
    • How many entities are correctly extracted by each method?
    • How many relations are correctly extracted by each method?
    • Contribution and retention of each method in the final knowledge graph, including human annotation.

Example of the interface, you only need to register, and you can access it freely.

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Development

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements.dev.txt

pip install -e .

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Citation

If you find this package useful, please consider citing our work:

@misc{sun2024docs2kg,
    title = {Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models},
    author = {Qiang Sun and Yuanyi Luo and Wenxiao Zhang and Sirui Li and Jichunyang Li and Kai Niu and Xiangrui Kong and Wei Liu},
    year = {2024},
    eprint = {2406.02962},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}