Taxonomies Supporting Orientation, Navigation and Auditing of Terminologies

描述(由申请人提供)： 我们已经看到了满足以下要求的一代医学术语的出现 关系的系统继承。例如SNOMED CT(The 医学系统化术语-临床术语)，美国国家癌症研究所 同义词表(NCIT)、Kaiser的融合医学术语(CMT)、退伍军人事务部内部 企业术语、解剖学基础模型(FMA)和医学 实体词典(MED)。这些术语具有相当大的规模和复杂性。 因此，面向用户是很困难的，特别是考虑到更多的知识是 不断地被添加到它们中。定向和导航能力对于 有效的术语维护和使用(例如，在决策支持系统中， 患者记录和医疗保健管理系统)。一个人不能合理地 期望在没有它们的情况下可靠地维护或使用术语。我们建议设计一种 用于派生新术语视图的结构抽象方法，称为 “分类学。”这些将构成支持用户导向的新技术的基础 术语的链接和导航。这些分类法将进一步有助于有效的审计。 将开发若干不同级别的分类法。我们的方法将 利用IS-A关系层次结构和伴随的系统关系 对这一代术语的继承。它们将基于新的分区 技术，这将把大的概念集合分解成更小的单元 在结构和语义上相似的概念，可以更容易地处理和 明白了。一种分区技术将基于相似的关系结构， 由此衍生出一种称为“区域分类法”的抽象网络。另一个 将在术语的ISA中进一步利用基于共同祖先的语义相似性 导致更细粒度的“p-Area分类法”的层次结构。附加分区 技术-导致进一步细化的分类-将专注于其他结构 术语的特征，例如获取模式区域。我们的抽象 方法论将是通用的，适用于满足以下要求的各种术语 系统传承。我们将使用SNOMED和NCIT的基因组层次结构作为 试验台。我们将通过以下方式演示基于分类法的方法的实用性 根据基本术语定义各种复杂性度量 网络和跟踪术语的演变。

DESCRIPTION (provided by applicant): We have seen the emergence of a generation of medical terminologies satisfying systematic inheritance of relationships. Examples include SNOMED CT (the Systematized Nomenclature of Medicine - Clinical Terms), the National Cancer Institute Thesaurus (NCIT), Kaiser's Convergent Medical Terminology (CMT), the VA's internal enterprise terminology, the Foundational Model of Anatomy (FMA), and the Medical Entities Dictionary (MED). These terminologies are of substantial size and complexity. Due to this, user orientation is difficult, especially given the fact that more knowledge is continually being added to them. Orientation and navigation capabilities are essential for effective terminology maintenance and usage (for example, in decision-support systems, patient records, and healthcare administrative systems). One cannot reasonably be expected to maintain or use a terminology reliably without them. We propose to design structural abstraction methodologies to derive novel terminological views called "taxonomies." These will form the bases for new techniques to support user orientation to and navigation of terminologies. The taxonomies will further aid in efficient auditing. A number of different levels of taxonomy will be developed. Our methodologies will utilize the IS-A relationship hierarchies and accompanying systematic relationship inheritance of this generation of terminologies. They will be based on new partitioning techniques, which will break down large collections of concepts into smaller units of structurally and semantically similar concepts that can be more easily handled and comprehended. One partitioning technique will be based on similar relationship structure, leading to the derivation of an abstraction network call the "area taxonomy." Another will further utilize semantic similarity based on common ancestry in the terminology's ISA hierarchy leading to the finer-grained "p-area taxonomy." Additional partitioning techniques---leading to further refined taxonomies---will focus on other structural features of terminologies, such as obtainment-pattern regions. Our abstraction methodologies will be general and applicable to a wide range of terminologies satisfying systematic inheritance. We will use SNOMED and the NCIT's genomics hierarchies as test-beds. We will demonstrate the utility of our taxonomy-based methodologies by defining various complexity measures with respect to the underlying terminology networks and by tracking terminology evolution.