Imperial College London

ProfessorPeterChilds

Faculty of EngineeringDyson School of Design Engineering

Head of the School of Design Engineering
 
 
 
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Contact

 

+44 (0)20 7594 7049p.childs Website CV

 
 
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Location

 

Studio 1, Dyson BuildingDyson BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Shi:2017:10.1115/1.4037649,
author = {Shi, F and Chen, L and Han, J and Childs, P},
doi = {10.1115/1.4037649},
journal = {Journal of Mechanical Design, Transactions of the ASME},
title = {A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval},
url = {http://dx.doi.org/10.1115/1.4037649},
volume = {139},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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 largescale 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.
AU - Shi,F
AU - Chen,L
AU - Han,J
AU - Childs,P
DO - 10.1115/1.4037649
PY - 2017///
SN - 1050-0472
TI - A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval
T2 - Journal of Mechanical Design, Transactions of the ASME
UR - http://dx.doi.org/10.1115/1.4037649
UR - http://mechanicaldesign.asmedigitalcollection.asme.org/article.aspx?articleid=2650709
VL - 139
ER -