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Engineering Knowledge Graph for Keyword Discovery in Patent Search

Published online by Cambridge University Press:  26 July 2019

Serhad Sarica*
Singapore University of Technology and Design, Engineering Product Development Pillar;
Binyang Song
Singapore University of Technology and Design, Engineering Product Development Pillar;
En Low
Singapore University of Technology and Design
Jianxi Luo
Singapore University of Technology and Design, Engineering Product Development Pillar;
Contact: Sarica, Serhad, Singapore University of Technology and Design Engineering Product Development, Singapore,


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Patent retrieval and analytics have become common tasks in engineering design and innovation. Keyword-based search is the most common method and the core of integrative methods for patent retrieval. Often searchers intuitively choose keywords according to their knowledge on the search interest which may limit the coverage of the retrieval. Although one can identify additional keywords via reading patent texts from prior searches to refine the query terms heuristically, the process is tedious, time-consuming, and prone to human errors. In this paper, we propose a method to automate and augment the heuristic and iterative keyword discovery process. Specifically, we train a semantic engineering knowledge graph on the full patent database using natural language processing and semantic analysis, and use it as the basis to retrieve and rank the keywords contained in the retrieved patents. On this basis, searchers do not need to read patent texts but just select among the recommended keywords to expand their queries. The proposed method improves the completeness of the search keyword set and reduces the human effort for the same task.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
© The Author(s) 2019


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