计算机科学
人工智能
相似性(几何)
领域(数学)
疾病
动作(物理)
分类
对象(语法)
机器学习
认知
心理学
作者
Rongrong Li,Xuefeng Wang,Yuqin Liu,Shuo Zhang
出处
期刊:IEEE Transactions on Engineering Management
[Institute of Electrical and Electronics Engineers]
日期:2021-01-15
卷期号:: 1-14
标识
DOI:10.1109/tem.2020.3047370
摘要
This article presents an improved method of measuring technology similarity by introducing a subject-action-object (SAO) analysis that uses the feature weights of semantic structure and professional vocabulary to measure technology similarity in the medical field. First, the SAO semantic structures are extracted and cleaned; then the structures related to technology are identified using a semantic network of the unified medical language system (UMLS). Second, the similarity between the SAO semantic structures is evaluated using semantic information from the Metathesaurus of the UMLS. Third, the feature weights of the SAO semantic structure are introduced to represent the importance of the patentees’ technology features. Finally, using the SAO and weight information, each patentee's vector is constructed to measure the technology complementarity between different patentees. This study conducts empirical research on Alzheimer's disease. The results indicate that the propose method for measuring technology similarity enables finer distinctions with more reliable outcomes than the traditional methods that are based on keywords and international patent classification.
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