提出医学概念质量评估指标，提升知识库构建效率。
What makes a good concept anyway ?
- 融合词长、频次、词性等多因素构建评分模型
- 模型与专家判断达50.67%一致性（Krippendorff's alpha）
- 适合医学知识图谱构建者参考使用
理想的医学本体应全面准确覆盖领域，但大型本体难以构建、理解与维护。因此，向现有本体添加新概念（常为多词概念）需谨慎，仅应引入“优质”概念。然而，“优质”概念的定义尚不明确。本研究识别影响医学专家判断概念质量的关键因素，并整合为单一评分指标。这些因素包括：概念名称的词数、在医学文献中的出现频率、组成词的语法类别，以及术语映射到特定外语后的简洁性。通过贝叶斯优化调整各因素权重，使指标与三位医学专家判断的吻合度最大化。结果表明，该指标与专家判断的整体一致性达到50.67%（以Krippendorff's alpha衡量）。
原文摘要 · Abstract (English)
A good medical ontology is expected to cover its domain completely and correctly. On the other hand, large ontologies are hard to build, hard to understand, and hard to maintain. Thus, adding new concepts (often multi-word concepts) to an existing ontology must be done judiciously. Only "good" concepts should be added; however, it is difficult to define what makes a concept good. In this research, we propose a metric to measure the goodness of a concept. We identified factors that appear to influence goodness judgments of medical experts and combined them into a single metric. These factors include concept name length (in words), concept occurrence frequency in the medical literature, and syntactic categories of component words. As an added factor, we used the simplicity of a term after mapping it into a specific foreign language. We performed Bayesian optimization of factor weights to achieve maximum agreement between the metric and three medical experts. The results showed that our metric had a 50.67% overall agreement with the experts, as measured by Krippendorff's alpha.
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