An improved patent similarity measurement based on entities and semantic relations

Patent similarity measurement, as one of the fundamental building blocks for patent analysis, is able to derive technical intelligence efficiently, but also can detect the risk of infringement and evaluate whether the invention meets the criteria of novelty and innovation. However, traditional approaches make implicitly several assumptions, such as bag of words in each component, semantic direction irrelevance and so on. In order to relax these assumptions, this study proposes an improved methodology on the basis of entities and semantic relations (functional and non-functional relations), which takes semantic direction of each sequence structure and the word order information of each component into consideration. Meanwhile, an algorithm for calculating the global importance of each sequence structure is put forward. Finally, to verify the effectiveness and performance of the improved semantic analysis, a case study is conducted on the thin film head subfield in the field of hard disk drive. Extensive experimental results show that our approach is significantly more accurate. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

An improved methodology based on entities and semantic relations is proposed in this study, which considers semantic direction and word order information, effectively improving the accuracy and performance of patent similarity measurement.