# MEDAKA: Construction of Biomedical Knowledge Graphs Using Large Language Models

arXiv: https://arxiv.org/abs/2509.26128 · HTML: https://mlsanity.com/paper/2509.26128/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes](https://mlsanity.com/paper/2301.12473/) (2023)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge](https://mlsanity.com/paper/2502.13010/) (2025)
- [A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support](https://mlsanity.com/paper/2503.23029/) (2025)
- [Analysis of Drug repurposing Knowledge graphs for Covid-19](https://mlsanity.com/paper/2212.03911/) (2022)

## Built on by (most cited first, 1 of 1)

- [PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks](https://mlsanity.com/paper/2608.02692/) (2026)

## Abstract (arXiv)

Knowledge graphs (KGs) are increasingly used to represent biomedical information in structured, interpretable formats. However, existing biomedical KGs often focus narrowly on molecular interactions or adverse events, overlooking the rich data found in drug leaflets. In this work, we present (1) a hackable, end-to-end pipeline to create KGs from unstructured online content using a web scraper and an LLM; and (2) a curated dataset, MEDAKA, generated by applying this method to publicly available drug leaflets. The dataset captures clinically relevant attributes such as side effects, warnings, contraindications, ingredients, dosage guidelines, storage instructions and physical characteristics. We evaluate it through manual inspection and with an LLM-as-a-Judge framework, and compare its coverage with existing biomedical KGs and databases. We expect MEDAKA to support tasks such as patient safety monitoring and drug recommendation. The pipeline can also be used for constructing KGs from unstructured texts in other domains. Code and dataset are available at https://github.com/medakakg/medaka.