Sculpting the UMLS Refined Semantic Network.
Sculpting the UMLS Refined Semantic Network.
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DOI：
10.5210/ojphi.v6i2.5412
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发表时间：
2014
影响因子：
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通讯作者：
Geller J
中科院分区：
文献类型：
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作者：
He Z;Morrey CP;Perl Y;Elhanan G;Chen L;Chen Y;Geller J
用于UMLS的精炼语义网络（RSN）先前被引入以补充UMLS语义网络（SN）。RSN将UMLS元词库（META）划分为不相交的概念组。每个这样的组在语义上是统一的。然而，RSN最初比SN大一个数量级，这是不期望的，因为为了有用，语义网络应该是紧凑的。RSN中的大多数语义类型表示UMLS SN中语义类型的组合。这种“组合语义类型”被称为交集语义类型（IST）。许多IST被分配给很少的概念。此外，在对这些概念进行检查时，发现了许多语义类型分配不一致的地方，在纠正这些不一致之后，许多IST消失了，其中一些与UMLS规则相矛盾，这使得RSN变小了。作者进行了一项纵向研究，目的是缩小RSN的尺寸，使其变得紧凑。这一目标是通过纠正UMLS中IST分配中的不一致和错误来实现的，这还有助于识别和纠正在公共卫生领域广泛使用的源术语中的歧义、不一致和错误。在本文中，我们讨论的过程和步骤中采用的纵向研究和中间结果的不同阶段。雕刻过程包括删除冗余的语义类型分配，扩展语义类型分配，以及通过审计小范围的IST来删除非法的IST。然而，本文的重点不是在这一过程中采用的审计方法，因为它们在早期的出版物中已经介绍过，而是采用它们的战略，以便将RSN转变为一个紧凑的网络。在本文中，我们还对2013年AA版UMLS中的168个"小型IST"进行了全面审计，以完成纵向研究。多年来，人们发现UMLS的编辑引入了一些新的不一致之处，导致重新引入了由于之前的更正而已经被消除的不必要的IST。因此，将RSN转换为一个涵盖UMLS所有必要类别的紧凑网络的速度放慢了。对2013年AA版UMLS的审计建议的更正实现了与UMLS SN相同量级的紧凑RSN。临时支助组的数目已减少到336个。我们还演示了如何审计UMLS概念的语义类型分配可以暴露UMLS源术语中的其他建模错误，例如，SNOMED CT、LOINC和RxNORM对健康信息学非常重要。否则，这些错误将被隐藏起来。我们希望UMLS管理员在维护和扩展UMLS时执行所有要求的更正，并沿着使用RSN和SN。如果使用正确，RSN将支持防止将不一致的语义类型分配意外引入UMLS。此外，这样的RSN将支持暴露卫生信息学术语中的其他隐藏错误和不一致，这些术语是UMLS的来源。值得注意的是，RSN的开发实现了UMLS的更深、更精细的语义网络，这是其设计者最初设想但尚未实现的。
The Refined Semantic Network (RSN) for the UMLS was previously introduced to complement the UMLS Semantic Network (SN). The RSN partitions the UMLS Metathesaurus (META) into disjoint groups of concepts. Each such group is semantically uniform. However, the RSN was initially an order of magnitude larger than the SN, which is undesirable since to be useful, a semantic network should be compact. Most semantic types in the RSN represent combinations of semantic types in the UMLS SN. Such a “combination semantic type” is called Intersection Semantic Type (IST). Many ISTs are assigned to very few concepts. Moreover, when reviewing those concepts, many semantic type assignment inconsistencies were found. After correcting those inconsistencies many ISTs, among them some that contradicted UMLS rules, disappeared, which made the RSN smaller. The authors performed a longitudinal study with the goal of reducing the size of the RSN to become compact. This goal was achieved by correcting inconsistencies and errors in the IST assignments in the UMLS, which additionally helped identify and correct ambiguities, inconsistencies, and errors in source terminologies widely used in the realm of public health. In this paper, we discuss the process and steps employed in this longitudinal study and the intermediate results for different stages. The sculpting process includes removing redundant semantic type assignments, expanding semantic type assignments, and removing illegitimate ISTs by auditing ISTs of small extents. However, the emphasis of this paper is not on the auditing methodologies employed during the process, since they were introduced in earlier publications, but on the strategy of employing them in order to transform the RSN into a compact network. For this paper we also performed a comprehensive audit of 168 “small ISTs” in the 2013AA version of the UMLS to finalize the longitudinal study. Over the years it was found that the editors of the UMLS introduced some new inconsistencies that resulted in the reintroduction of unwarranted ISTs that had already been eliminated as a result of their previous corrections. Because of that, the transformation of the RSN into a compact network covering all necessary categories for the UMLS was slowed down. The corrections suggested by an audit of the 2013AA version of the UMLS achieve a compact RSN of equal magnitude as the UMLS SN. The number of ISTs has been reduced to 336. We also demonstrate how auditing the semantic type assignments of UMLS concepts can expose other modeling errors in the UMLS source terminologies, e.g., SNOMED CT, LOINC, and RxNORM that are important for health informatics. Such errors would otherwise stay hidden. It is hoped that the UMLS curators will implement all required corrections and use the RSN along with the SN when maintaining and extending the UMLS. When used correctly, the RSN will support the prevention of the accidental introduction of inconsistent semantic type assignments into the UMLS. Furthermore, this way the RSN will support the exposure of other hidden errors and inconsistencies in health informatics terminologies, which are sources of the UMLS. Notably, the development of the RSN materializes the deeper, more refined Semantic Network for the UMLS that its designers envisioned originally but had not implemented.