New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network

Andrew Rodriguez, Byunghoon Kim, Mehmet Turkoz, Jae Min Lee, Byoung Youl Coh, Myong K. Jeong

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Being able to effectively measure similarity between patents in a complex patent citation network is a crucial task in understanding patent relatedness. In the past, techniques such as text mining and keyword analysis have been applied for patent similarity calculation. The drawback of these approaches is that they depend on word choice and writing style of authors. Most existing graph-based approaches use common neighbor-based measures, which only consider direct adjacency. In this work we propose new similarity measures for patents in a patent citation network using only the patent citation network structure. The proposed similarity measures leverage direct and indirect co-citation links between patents. A challenge is when some patents receive a large number of citations, thus are considered more similar to many other patents in the patent citation network. To overcome this challenge, we propose a normalization technique to account for the case where some pairs are ranked very similar to each other because they both are cited by many other patents. We validate our proposed similarity measures using US class codes for US patents and the well-known Jaccard similarity index. Experiments show that the proposed methods perform well when compared to the Jaccard similarity index.

Original languageEnglish (US)
Pages (from-to)565-581
Number of pages17
JournalScientometrics
Volume103
Issue number2
DOIs
StatePublished - May 1 2015

All Science Journal Classification (ASJC) codes

  • Social Sciences(all)
  • Computer Science Applications
  • Library and Information Sciences

Keywords

  • Adjacency matrix
  • Co-citation
  • Indirect citation
  • Jaccard similarity index
  • Patent citation network
  • Similarity measure
  • US class code

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