Bone regeneration is a complex, tightly regulated process that is central to orthopedics, dentistry, and regenerative medicine. Research has traditionally emphasized osteogenesis, osteoinduction, and osteoconduction; more recently, angiogenic–osteogenic coupling and immune regulation have emerged as equally critical. Rapid growth in the number of relevant cell types, in vivo models, and biomaterial strategies—together with an expanding literature—makes it increasingly difficult to synthesize evidence across studies. Here, we present the Knowledge Graph of Bone Regeneration (KGBR), an AI-friendly framework with a literature-processing workflow that converts literature-derived information into a knowledge-graph data model. Using ontology-aligned semantic search, KGBR supports the exploration of bone–immune interactions and provides a structured synthesis of mechanisms and dominant themes in bone regeneration research. By representing the literature as a single interconnected semantic network, KGBR facilitates integration of existing knowledge and supports hypothesis generation. We anticipate that the KGBR data model will provide a foundation for knowledge-based analytics and decision support in bone regeneration.
Liu et al. (Fri,) studied this question.
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