新稀疏矩阵算法让自组织映射完整解析医学文献库
Novel sparse matrix algorithm expands the feasible size of a self-organizing map of the knowledge indexed by a database of peer-reviewed medical literature
- 设计稀疏矩阵乘法新算法，突破内存与计算瓶颈
- 首次实现对全量Medline数据的自组织映射
- 适合医学知识图谱构建与动态更新的研究者
以往对Medline数据库的映射工作受限于现有算法在内存和计算上的指数级增长需求，仅能处理数据子集。本文提出一种新型稀疏矩阵乘法算法，使自组织映射（SOM）得以应用于整个Medline数据集，从而构建更完整的医学知识图谱。该算法还提升了自组织映射随数据更新而迭代的可行性。
原文摘要 · Abstract (English)
Past efforts to map the Medline database have been limited to small subsets of the available data because of the exponentially increasing memory and processing demands of existing algorithms. We designed a novel algorithm for sparse matrix multiplication that allowed us to apply a self-organizing map to the entire Medline dataset, allowing for a more complete map of existing medical knowledge. The algorithm also increases the feasibility of refining the self-organizing map to account for changes in the dataset over time.
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