PrimeKG-CL supplies the first continual graph learning benchmark using authentic temporal snapshots from nine biomedical databases, showing strong interactions between embedding decoders and learning strategies plus limits of standard metrics on retention versus forgetting.
Building a knowledge graph to enable precision medicine.Scientific Data, 10(1):67
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CMKL delivers a 60% gain in average precision on continual entity classification in a 129K-entity biomedical KG benchmark by fusing multimodal features and protecting against modality-specific forgetting, while relationship prediction stays comparable to baselines.
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PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
PrimeKG-CL supplies the first continual graph learning benchmark using authentic temporal snapshots from nine biomedical databases, showing strong interactions between embedding decoders and learning strategies plus limits of standard metrics on retention versus forgetting.
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CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
CMKL delivers a 60% gain in average precision on continual entity classification in a 129K-entity biomedical KG benchmark by fusing multimodal features and protecting against modality-specific forgetting, while relationship prediction stays comparable to baselines.