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Patent-KG: Patent Knowledge Graph Extraction for Engineering Design

Published online by Cambridge University Press:  26 May 2022

H. Zuo ,
Y. Yin  and
P. Childs
H. Zuo*
Affiliation:
Imperial College London, United Kingdom
Y. Yin
Affiliation:
Imperial College London, United Kingdom
P. Childs
Affiliation:
Imperial College London, United Kingdom
*
[email protected]

Abstract

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This paper builds a patent-based knowledge graph, patent-KG, to represent the knowledge facts in patents for engineering design. The arising patent-KG approach proposes a new unsupervised mechanism to extract knowledge facts in a patent, by searching the attention graph in language models. The extracted entities are compared with other benchmarks in the criteria of recall rate. The result reaches the highest 0.8 recall rate in the standard list of mechanical engineering related technical terms, which means the highest coverage of engineering words.

Keywords

knowledge representationsartificial intelligence (AI)data-driven design
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 821 - 830
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), 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.
Copyright
The Author(s), 2022.

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