Staff Similarity Computation in Technology Innovation Team

Wei Sun; Yang Yu

doi:10.4028/www.scientific.net/AMR.204-210.1771

Paper Titles

Experimental Study on the Plastic Collapse Load of Elbows Subjected to In-Plane Opening Bending Moment
p.1755

The Information Optimal Algorithm Based on Poly-Scaled Wavelet Wraps and Wavelet Frames with Finite Support
p.1759

A Study of Orthogonal Vector-Valued Binary Wavelets and Wavelet Packs with Integer Dilation Constant
p.1763

Engine Cells Performance Characteristics Modeling and Evaluating
p.1767

Design on the Mode of Urban Renewal Public Service Platform
p.1775

Topological Approaches to the Second Type of Covering-Based Rough Sets
p.1781

Decompositions of Fuzzy Continuity and Fuzzy Complete Continuity
p.1785

Study on Performance for Fiber Asphalt Mixture Resistance to Water Damage
p.1789

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 204-210Staff Similarity Computation in Technology...

Staff Similarity Computation in Technology Innovation Team

Abstract:

The main objective of this investigation is to explore new similarity algorithms of staff similarity in technology innovation team. First, this paper proposes the knowledge representation model of technology staff based on network, and the cliques after clustering according to network feature expresses the sub-fields. Second, from the view of knowledge contained in technology staff, this paper proposes the similarity algorithm based on VSM and the similarity algorithm based on sub-field. Finally, we use the staff classification of one technology innovation team as case study. The experiment results reveal that the similarity of the new methods is accurate than that of the old method, and the information obtained by the new methods is more than that obtained by the old method.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 204-210)

Pages:

1771-1774

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.204-210.1771

Citation:

Cite this paper

Online since:

February 2011

Authors:

Wei Sun, Yang Yu

Keywords:

Knowledge Network, Similarity Algorithm, Technology Innovation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Margarita Sordo, Matvey B. Palchuk. Grouped knowledge elements [J]. Clinical Decision Support, 2007: 325-343.

DOI: 10.1016/b978-012369377-8/50016-7

Google Scholar

[2] Byron Marshall, Hsinchun Chen, Therani Madhusudan. Matching knowledge elements in concept maps using a similarity flooding algorithm [J]. Decision Support Systems, 2006, 42(3): 1290-1306.

DOI: 10.1016/j.dss.2005.10.009

Google Scholar

[3] Qinghua Chen, Dinghua Shi. The modeling of scale-free networks [J]. Physica A, 2004, 335(1-2): 240-248.

Google Scholar

[4] Barabasi A L, Bonabeau E. Scale-free networks [J]. Scientific American, 2003, 5(1): 50-59.

Google Scholar

[5] S Boccalettia, V Latorab, Y Morenod. Complex networks: Structure and dynamics [J]. Physics Reports, 2006, 424: 175-308.

Google Scholar

[6] Sanjay Jain, Sandeep Krishna. Emergence and growth of complex networks in adaptive systems [J]. Computer Physics Communications, 1999, 121-122: 116-121.

DOI: 10.1016/s0010-4655(99)00293-3

Google Scholar

[7] Kazuhiro Takemoto, Chikoo Oosawa and Tatsuya Akutsu. Structure of n-clique networks embedded in a complex network [J]. Physica A: Statistical Mechanics and its Applications, 2007, 380: 665-672.

DOI: 10.1016/j.physa.2007.02.042

Google Scholar

[8] Salton G, Yang C S. On the specification of term values in automatic indexing [J]. Journal of Documentation , 1973 , 29(4): 351-372.

DOI: 10.1108/eb026562

Google Scholar