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Research Papers: D3 Methods

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

[+] Author and Article Information
Feng Shi

Engineering Design Group,
Dyson School of Design Engineering,
Imperial College London,
South Kensington,
London SW7 1NA, UK
e-mail: f.shi14@imperial.ac.uk

Liuqing Chen

Engineering Design Group,
Dyson School of Design Engineering,
Imperial College London,
South Kensington,
London SW7 1NA, UK
e-mail: l.chen15@imperial.ac.uk

Ji Han

Engineering Design Group,
Dyson School of Design Engineering,
Imperial College London,
South Kensington,
London SW7 1NA, UK
e-mail: j.han14@imperial.ac.uk

Peter Childs

Professor
Fellow ASME
Head of the School,
Dyson School of Design Engineering,
Imperial College London,
South Kensington,
London SW7 1NA, UK
e-mail: p.childs@imperial.ac.uk

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 10, 2017; final manuscript received August 12, 2017; published online October 2, 2017. Assoc. Editor: Ying Liu.

J. Mech. Des 139(11), 111402 (Oct 02, 2017) (14 pages) Paper No: MD-17-1117; doi: 10.1115/1.4037649 History: Received February 10, 2017; Revised August 12, 2017

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

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Figures

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Fig. 1

The framework of proposed semantic network analysis for design information retrieval

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Fig. 2

The growing process of unsupervised learning network

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Fig. 3

A top-down concept structure arranged by the node strength property

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Fig. 4

Histogram of the shortest path distances in probability analysis

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Fig. 5

Histogram of the shortest path distances in velocity analysis

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Fig. 6

Node strength on the most probable paths and fastest paths

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Fig. 7

Top 20 relevant concepts from probability and velocity analysis: (a) Probability analysis ranked by correlation degree Rp and (b) velocity analysis ranked by correlation degree Rv

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Fig. 8

The whole three-phase retrieval process

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Fig. 9

Node strength of retrieved paths in the three retrieval phases

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