Automated Extraction of Function Knowledge from Text

[+] Author and Article Information
Hyunmin Cheong

Autodesk Research, 210 King St East, Toronto, ON, Canada M5G 1P7

Wei Li

Huawei Technologies Canada, 19 Allstate Pkwy, Markham, ON, Canada L3R 5A4

Adrian Cheung

Facebook, 1101 Dexter Ave N, Seattle, WA 98109, USA

Andy Nogueira

Autodesk Research, 210 King St East, Toronto, ON, Canada M5G 1P7

Francesco Iorio

Autodesk Research, 210 King St East, Toronto, ON, Canada M5G 1P7

1Corresponding author.

ASME doi:10.1115/1.4037817 History: Received February 23, 2017; Revised June 07, 2017


This paper presents a method to automatically extract function knowledge from natural language text. The extraction method uses syntactic rules to extract subject-verb-object triplets from parsed text. Then, the Functional Basis taxonomy, WordNet, and word2vec were leveraged to classify the triplets as artifact-function-energy flow knowledge. For evaluation, the function definitions associated with 30 most frequent artifacts compiled in a human-constructed knowledge base, Oregon State University's Design Repository (DR), were compared to those extracted from 4953 Wikipedia pages classified under the category "Machines" using the method developed. The method found function definitions for 66% of the test artifacts. For those artifacts found, 50% of the function definitions identified were compiled in the DR. In addition, 75% of the most frequent function definitions found by the method were also defined in the DR. The results demonstrate the potential of the current work in enabling automated construction of function knowledge repositories.

Copyright (c) 2017 by ASME
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