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

 

10 Princes Gardens10-12 Prince's GardensSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Shi:2017,
author = {Shi, F and Chen, L and HAN, JI and CHILDS, PETER},
publisher = {ASME},
title = {A data-driven self-learning network analysis for ontology-based design knowledge retrieval},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - With the advent of the big-data era, massive information stored in electronic and digitalforms on the internet become valuable resources for knowledge discovery in engineeringdesign. Traditional document retrieval method based on document indexing focuses onretrieving individual documents related to the query, but is incapable of discovering thevarious associations between individual knowledge concepts. Ontology-based technologies,which can extract the inherent relationships between concepts by using advancedtext mining tools, can be applied to improve design information retrieval in the largescaleunstructured textual data environment. However, few of the public available ontologydatabase stands on a design and engineering perspective to establish the relationsbetween knowledge concepts. This paper develops a “WordNet” focusing on design andengineering associations by integrating the text mining approaches to construct an unsupervisedlearning ontology network. Subsequent probability and velocity network analysisare applied with different statistical behaviors to evaluate the correlation degreebetween concepts for design information retrieval. The validation results show that theprobability and velocity analysis on our constructed ontology network can help recognizethe high related complex design and engineering associations between elements. Finally,an engineering design case study demonstrates the use of our constructed semantic networkin real-world project for design relations retrieval.
AU - Shi,F
AU - Chen,L
AU - HAN,JI
AU - CHILDS,PETER
PB - ASME
PY - 2017///
TI - A data-driven self-learning network analysis for ontology-based design knowledge retrieval
ER -