Fine-tuning NER for Triplet Extraction in Medical Knowledge Graph Construction

Fine-tuning NER for Triplet Extraction in Medical Knowledge Graph Construction

dependency parsing; medical knowledge graph; named entity recognition; part-of-speech tagging; triplets.

This study presents a new approach for constructing a medical knowledge graph using Named Entity Recognition (NER) to identify entities such as diseases, drugs, or medical procedures, alongside part-of-speech (POS) tagging and dependency parsing to determine wordsthat function as verbs and roots. These extracted words are then used as relations between entities, forming triplets in the format (entity, relation, entity). While the knowledge graph provides a structured
representation of medical information, the evaluation primarily reflects the performance of the underlying NLP pipeline (NER, POS tagging, and dependency parsing) used to generate the triplets. Quantitative evaluation was performed using
metrics such as precision, recall, and F1-score to assess the accuracy and completeness of entity and relation extraction. The qualitative evaluation involved medical domain experts to assess the relevance and validity of the relationships
derived. The results indicate that fine-tuning a pre-trained model for NER and leveraging a pre-trained model for POS tagging and dependency parsing can
effectively generate accurate triplets for constructing a medical knowledge graph.
This approach demonstrated strong performance, achieving high evaluation scores in both quantitative and qualitative evaluations.

Richard Reinhart & Masayu Leylia Khodra

DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.2.1

IRCS-ITB

6 November 2025

Sri Wahyuni

ISSN : 23375787

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English

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