# Descriptive Knowledge Graph in Biomedical Domain

arXiv: https://arxiv.org/abs/2310.11681 · HTML: https://mlsanity.com/paper/2310.11681/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation](https://mlsanity.com/paper/2007.00576/) (2020)
- [Interactive Extractive Search over Biomedical Corpora](https://mlsanity.com/paper/2006.04148/) (2020)
- [Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach](https://mlsanity.com/paper/1804.10877/) (2018)
- [Open Relation Modeling: Learning to Define Relations between Entities](https://mlsanity.com/paper/2108.09241/) (2021)
- [Open-Domain Question-Answering for COVID-19 and Other Emergent Domains](https://mlsanity.com/paper/2110.06962/) (2021)
- [DEER: Descriptive Knowledge Graph for Explaining Entity Relationships](https://mlsanity.com/paper/2205.10479/) (2022)
- [DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships](https://mlsanity.com/paper/2212.10545/) (2022)
- [CCGen: Explainable Complementary Concept Generation in E-Commerce](https://mlsanity.com/paper/2305.11480/) (2023)
- [VER: Unifying Verbalizing Entities and Relations](https://mlsanity.com/paper/2211.11093/) (2022)

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

- [End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction](https://mlsanity.com/paper/2406.03790/) (2024)

## Abstract (arXiv)

We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences as a relational graph, enabling researchers to explore closely related biomedical entities (e.g., diseases treated by a chemical) or indirectly connected entities (e.g., potential drugs for treating a disease). Our system also uses ChatGPT and a fine-tuned relation synthesis model to generate concise and reliable descriptive sentences from retrieved information, reducing the need for extensive human reading effort. With our system, researchers can easily obtain both high-level knowledge and detailed references and interactively steer to the information of interest. We spotlight the application of our system in COVID-19 research, illustrating its utility in areas such as drug repurposing and literature curation.