UMLS语义命题是用三元组表示的最小语义化知识单位，其主语和宾语都是UMLS超级叙词表中的概念，谓词是UMLS语义网络中的语义关系。UMLS语义命题的抽取过程涉及浅层句法分析、概念映射、谓词识别与语义命题生成等环节。两种以UMLS语义命题为基础的医学信息资源聚合方法——用知识单元作为资源单位的聚合方法和用文档关联数据作为资源单位的聚合方法，其聚合结果分别是知识网络和文档网络。
UMLS semantic propositions are the smallest units of semantic knowledge represented as triples, in which subjects and objects are concepts from the UMLS metathesaurus and predicates are semantic relations from the UMLS semantic network. The extraction process of UMLS semantic propositions involves the shallow parsing, concept mapping, predicate identification and generation of semantic proposition. This paper proposes two polymerization methods of medical information resources based on UMLS semantic propositions, which respectively regard knowledge units and linked document data as resource units. The polymerization results are respectively a knowledge network and a document network.
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