Data Overdose? Time for a Quadruple Shot: Knowledge Graph Construction using Enhanced Triple Extraction

TL;DR

This paper introduces a novel method for constructing biomedical knowledge graphs from PubMed abstracts using enhanced triple extraction with context variables, improving accuracy and enabling inference of new relationships.

Contribution

It presents a pipeline utilizing large language models and ontology-based methods to generate quadruples for knowledge graphs, incorporating context for improved semantic clarity.

Findings

Achieved an average cosine similarity of 0.874 for generated sentences from triples.

Enhanced triples with context variables showed increased similarity over ordinary triples.

Demonstrated the potential for LLMs to infer new relationships and connect knowledge clusters.

Abstract

The rapid expansion of publicly-available medical data presents a challenge for clinicians and researchers alike, increasing the gap between the volume of scientific literature and its applications. The steady growth of studies and findings overwhelms medical professionals at large, hindering their ability to systematically review and understand the latest knowledge. This paper presents an approach to information extraction and automatic knowledge graph (KG) generation to identify and connect biomedical knowledge. Through a pipeline of large language model (LLM) agents, the system decomposes 44 PubMed abstracts into semantically meaningful proposition sentences and extracts KG triples from these sentences. The triples are enhanced using a combination of open domain and ontology-based information extraction methodologies to incorporate ontological categories. On top of this, a context variable is included during extraction to allow the triple to stand on its own - thereby becoming `quadruples'. The extraction accuracy of the LLM is validated by comparing natural language sentences generated from the enhanced triples to the original propositions, achieving an average cosine similarity of 0.874. The similarity for generated sentences of enhanced triples were compared with generated sentences of ordinary triples showing an increase as a result of the context variable. Furthermore, this research explores the ability for LLMs to infer new relationships and connect clusters in the knowledge base of the knowledge graph. This approach leads the way to provide medical practitioners with a centralised, updated in real-time, and sustainable knowledge source, and may be the foundation of similar gains in a wide variety of fields.