# Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models

arXiv: https://arxiv.org/abs/2406.02962 · HTML: https://mlsanity.com/paper/2406.02962/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis](https://mlsanity.com/paper/2208.10970/) (2022)

## Built on by (most cited first, 9 of 9)

- [CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning](https://mlsanity.com/paper/2502.02390/) (2025)
- [SuperRAG: Beyond RAG with Layout-Aware Graph Modeling](https://mlsanity.com/paper/2503.04790/) (2025)
- [How to Mitigate Information Loss in Knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback](https://mlsanity.com/paper/2501.15378/) (2025)
- [MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph](https://mlsanity.com/paper/2508.12393/) (2025)
- [Large Language Models for Knowledge Graph Embedding: A Survey](https://mlsanity.com/paper/2501.07766/) (2025)
- [Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs](https://mlsanity.com/paper/2604.02477/) (2026)
- [SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration](https://mlsanity.com/paper/2505.02418/) (2025)
- [From PDF to RAG-Ready: Evaluating Document Conversion Frameworks for Domain-Specific Question Answering](https://mlsanity.com/paper/2604.04948/) (2026)
- [FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis](https://mlsanity.com/paper/2607.11464/) (2026)

## Abstract (arXiv)

Even for a conservative estimate, 80% of enterprise data reside in unstructured files, stored in data lakes that accommodate heterogeneous formats. Classical search engines can no longer meet information seeking needs, especially when the task is to browse and explore for insight formulation. In other words, there are no obvious search keywords to use. Knowledge graphs, due to their natural visual appeals that reduce the human cognitive load, become the winning candidate for heterogeneous data integration and knowledge representation. In this paper, we introduce Docs2KG, a novel framework designed to extract multimodal information from diverse and heterogeneous unstructured documents, including emails, web pages, PDF files, and Excel files. Dynamically generates a unified knowledge graph that represents the extracted key information, Docs2KG enables efficient querying and exploration of document data lakes. Unlike existing approaches that focus on domain-specific data sources or pre-designed schemas, Docs2KG offers a flexible and extensible solution that can adapt to various document structures and content types. The proposed framework unifies data processing supporting a multitude of downstream tasks with improved domain interpretability. Docs2KG is publicly accessible at https://docs2kg.ai4wa.com, and a demonstration video is available at https://docs2kg.ai4wa.com/Video.