Semantic refinement and error correction in large terminological knowledge bases
Semantic refinement and error correction in large terminological knowledge bases
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DOI：
10.1016/s0169-023x(02)00153-2
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发表时间：
2003-04-01
影响因子：
2.5
通讯作者：
Halper, M
中科院分区：
文献类型：
--
作者：
Geller, J;Gu, HY;Halper, M
捕获术语中概念的语义一直是人工智能中的一个重要问题。已经提出了一种两级方法，其中概念被分类为高级语义类型，这些类型构成概念语义的一部分。我们提出了一种算法方法来精炼这种两级术语网络。由此产生了一个由“纯”语义类型和交集类型组成的新网络。概念被唯一地重新分配给这些新类型。总的来说，这些类型形成了更好的概念抽象，每种类型都具有统一的语义。使用它们，可以更容易地检测分类错误。该方法应用于UMLS。(C) 2002 Elsevier Science B.V.版权所有
Capturing the semantics of concepts in a terminology has been an important problem in AI. A two-level approach has been proposed where concepts are classified into high-level semantic types, with these types constituting a portion of the concepts' semantics. We present an algorithmic methodology for refining such two-level terminologic networks. A new network is produced consisting of "pure" semantic types and intersection types. Concepts are uniquely re-assigned to these new types. Overall, these types form a better conceptual abstraction, with each exhibiting uniform semantics. Using them, it becomes easier to detect classification errors. The methodology is applied to the UMLS. (C) 2002 Elsevier Science B.V. All rights reserved.