Research Papers

Extracting the Structure of Design Information From Collaborative Tagging

[+] Author and Article Information
Jitesh H. Panchal1

School of Mechanical and Materials Engineering,  Washington State University, Pullman, WA 99164panchal@wsu.edu

Matthias Messer

 Innovationcenter Global Projects, Freudenberg Dichtungs-und Schwingungstechnik GmbH (FDS), Weinheim, Germany 69465Matthias.Messer@freudenberg-ds.com






Corresponding author.

J. Comput. Inf. Sci. Eng 11(4), 041007 (Nov 29, 2011) (11 pages) doi:10.1115/1.3617447 History: Received August 06, 2010; Revised March 29, 2011; Published November 29, 2011; Online November 29, 2011

Information representation in engineering design is currently dominated by top–down approaches such as taxonomies and ontologies. While top–down approaches provide support for computational reasoning, they are primarily limited due to their static nature, limited scope, and developer-centric focus. Bottom–up approaches, such as folksonomies, are emerging as means to address the limitations of top–down approaches. Folksonomies refer to collaborative classification by users who freely assign tags to design information. They are dynamic in nature, broad in scope, and are user focused. However, they are limited due to the presence of ambiguities and redundancies in the tags used by different people. Considering their complementary nature, the ideal approach is to use both top–down and bottom–up approaches in a synergistic manner. To facilitate this synergy, the goal in this paper is to present techniques for using dynamic folksonomies to extract global characteristics of the structure of design information, and to create hierarchies of tags that can guide the development of structured taxonomies and ontologies. The approach presented in this paper involves using (a) tools such as degree distribution and K-neighborhood connectivity analysis to extract the global characteristics of folksonomies and (b) set-based technique and hierarchical clustering to develop a hierarchy of tags. The approach is illustrated using data from a collective innovation platform that supports collaborative tagging for design information. It is shown that despite the flat nature of the folksonomies insights about the hierarchy in information can be gained. The effects of various parameters on the tag hierarchy are discussed. The approach has potential to be used synergistically with top–down approaches such as ontologies to support the next generation collaborative design platforms.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 9

Percentage of tags at Level 1 for different hierarchy levels of tags and content nodes

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Figure 8

Resulting number of concepts and tags after clustering

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Figure 7

Clusters of content nodes that share more than two tags

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Figure 6

Image representation of hierarchically clustered (a) content graph and (b) tags using co-occurrence similarity

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Figure 5

Effect of threshold parameters on the number of tags in (a) level 1 and (b) level 3

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Figure 4

Parts of the hierarchy of tags generated by the proposed approach

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Figure 3

(a) Maximum size and (b) number of components of k-neighborhoods

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Figure 2

Degree distribution of (a) tags and (b) content nodes in the bipartite graph

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Figure 1

An example of bipartite graphs and two weighted graphs derived from it




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