KoGNER: A Novel Framework for Knowledge Graph Distillation on Biomedical Named Entity Recognition
Journal:
arXiv
Published Date:
Mar 19, 2025
Abstract
Named Entity Recognition (NER) is a fundamental task in Natural Language
Processing (NLP) that plays a crucial role in information extraction, question
answering, and knowledge-based systems. Traditional deep learning-based NER
models often struggle with domain-specific generalization and suffer from data
sparsity issues. In this work, we introduce Knowledge Graph distilled for Named
Entity Recognition (KoGNER), a novel approach that integrates Knowledge Graph
(KG) distillation into NER models to enhance entity recognition performance.
Our framework leverages structured knowledge representations from KGs to enrich
contextual embeddings, thereby improving entity classification and reducing
ambiguity in entity detection. KoGNER employs a two-step process: (1) Knowledge
Distillation, where external knowledge sources are distilled into a lightweight
representation for seamless integration with NER models, and (2) Entity-Aware
Augmentation, which integrates contextual embeddings that have been enriched
with knowledge graph information directly into GNN, thereby improving the
model's ability to understand and represent entity relationships. Experimental
results on benchmark datasets demonstrate that KoGNER achieves state-of-the-art
performance, outperforming finetuned NER models and LLMs by a significant
margin. These findings suggest that leveraging knowledge graphs as auxiliary
information can significantly improve NER accuracy, making KoGNER a promising
direction for future research in knowledge-aware NLP.
