Research Papers: D3 Methods

Automated Extraction of Function Knowledge From Text

[+] Author and Article Information
Hyunmin Cheong

Autodesk Research,
210 King Street East,
Toronto, ON M5G 1P7, Canada
e-mail: hyunmin.cheong@autodesk.com

Wei Li

Autodesk Research,
Toronto, ON M5G 1P7, Canada
e-mail: Wei.Li.CRC@huawei.com

Adrian Cheung

Autodesk Research,
Toronto, ON M5G 1P7, Canada
e-mail: adrianmcheung@gmail.com

Andy Nogueira

Autodesk Research,
210 King Street East,
Toronto, ON M5G 1P7, Canada
e-mail: andy.nogueira@autodesk.com

Francesco Iorio

Autodesk Research,
210 King Street East,
Toronto, ON M5G 1P7, Canada
e-mail: francesco.iorio@autodesk.com

1Corresponding author.

2Present address: Huawei Technologies Canada, 19 Allstate Parkway, Markham, ON L3R 5A4, Canada.

3Present address: Facebook, 1101 Dexter Avenue N, Seattle, WA 98109.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 23, 2017; final manuscript received June 7, 2017; published online October 2, 2017. Assoc. Editor: Charlie C. L. Wang.

J. Mech. Des 139(11), 111407 (Oct 02, 2017) (9 pages) Paper No: MD-17-1170; 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 acquire subject-verb-object (SVO) triplets from parsed text. Then, the functional basis taxonomy, WordNet, and word2vec are utilized 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 the definitions identified by extraction the method from 4953 Wikipedia pages classified under the category “Machines.” 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 © 2017 by ASME
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Grahic Jump Location
Fig. 1

Overview of the SVO triplet acquisition process

Grahic Jump Location
Fig. 2

Overview of the energy flow classification method

Grahic Jump Location
Fig. 3

Proportion of function definitions assigned in design repository that are also identified by our method

Grahic Jump Location
Fig. 4

Proportion of function definitions identified by our method that are also found in design repository



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