Global knowledge graph of osteoporosis biomarkers based on large language model embeddings and complex network algorithms
Abstract
Background: This study applies advanced artificial intelligence technologies to reconstruct the global research landscape of osteoporosis (OP) biomarkers and to predict emerging nodes with potential clinical value.
Methods: Literature from the Web of Science Core Collection and PubMed over the past 20 years was integrated. The OpenAI text-embedding-3-large model mapped keywords into a high-dimensional semantic space, enabling deep clustering through cosine similarity and reducing polysemy. The Walktrap community detection algorithm and Callon strategic diagram were used to identify high-centrality, low-density themes in the "fourth quadrant." Gemini 2.5 Pro was applied to generate entity-relation triples, which were validated through a human-in-the-loop process and integrated into a refined knowledge graph. Topological indicators such as betweenness centrality were used to detect emerging potential nodes.
Results: A total of 1595 core publications were included, showing a significant linear rise in annual output. Nine major research themes were identified. The strategic diagram highlighted three high-potential domains: inflammation and immune regulation, detection technologies and biosensing, and gut microbiota and metabolism. Knowledge-graph analysis revealed oxidative stress as a key bridging node integrating immune, metabolic, and other systems. Several emerging nodes with strong connective potential were identified outside traditional hotspots, including molecular…
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