# Biomedical Event Extraction with Hierarchical Knowledge Graphs

arXiv: https://arxiv.org/abs/2009.09335 · HTML: https://mlsanity.com/paper/2009.09335/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study](https://mlsanity.com/paper/2308.14321/) (2023)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [SsciBERT: A Pre-trained Language Model for Social Science Texts](https://mlsanity.com/paper/2206.04510/) (2022)
- [Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations](https://mlsanity.com/paper/2106.12384/) (2021)
- [Event Extraction: A Survey](https://mlsanity.com/paper/2210.03419/) (2022)

## Abstract (arXiv)

Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.