Approaches to automatically extract affordances from patents
Chiarello, Filippo (1); Cirri, Ilenia (1); Melluso, Nicola (1); Fantoni, Gualtiero (1); Bonaccorsi, Andrea (1); Pavanello, Tommaso (2)
Type:
Year:
2019
Editor:
Wartzack, Sandro; Schleich, Benjamin; Gon
Author:
Chiarello, Filippo (1); Cirri, Ilenia (1); Melluso, Nicola (1); Fantoni, Gualtiero (1); Bonaccorsi, Andrea (1); Pavanello, Tommaso (2)
Series:
ICED
Institution:
Universit
Section:
Knowledge-based engineering
DOI number:
ISSN:
2220-4342
Abstract:
The importance of affordance in Engineering design is well established. Artifacts that are able to activate spontaneous and immediate users? reactions are considered the outcome of good design practice.
A huge effort has been made by researchers for understanding affordances: yet these efforts have been somewhat elusive. In particular, they have been limited to case studies and experimental studies, usually involving a small subset of affordances. No systematic effort has been carried out to list all known affordance effects. This paper offers preliminary steps for such an ambitious effort.
We propose a set of three different approaches of Natural Language Processing techniques to be used to extract meaningful affordance information from the full text of patents: 1) a simple word search, 2) a lexicon of affordances and 3) a rule-based system.
The results give in-depth measures of how rare affordances in patents are, and a fine grain analysis of the linguistical construction of affordances. Finally, we show an interesting output of our method, that has detected affordances for disabled people, showing the ability of our system to automatically collect design-relevant knowledge.
A huge effort has been made by researchers for understanding affordances: yet these efforts have been somewhat elusive. In particular, they have been limited to case studies and experimental studies, usually involving a small subset of affordances. No systematic effort has been carried out to list all known affordance effects. This paper offers preliminary steps for such an ambitious effort.
We propose a set of three different approaches of Natural Language Processing techniques to be used to extract meaningful affordance information from the full text of patents: 1) a simple word search, 2) a lexicon of affordances and 3) a rule-based system.
The results give in-depth measures of how rare affordances in patents are, and a fine grain analysis of the linguistical construction of affordances. Finally, we show an interesting output of our method, that has detected affordances for disabled people, showing the ability of our system to automatically collect design-relevant knowledge.
Keywords:
