An Approach to Expert Finding Based on Multi-Granularity Two-Tuple Linguistic Information

Ming Li

doi:10.4028/www.scientific.net/AMM.58-60.1317

Paper Titles

Simply Level Recalculation Using Statistical Approach
p.1294

Research on Coordinated Simulation of Medical Robot Arm Based on Simulink and RecurDyn
p.1300

Energy Wave Transmission and Control Analysis in Force Reflection Teleoperation Based on Interconnected Power Line
p.1306

Liver Image Segmentation Using Improved Watershed Method
p.1311

An Approach to Expert Finding Based on Multi-Granularity Two-Tuple Linguistic Information
p.1317

STRIDE – Based Risk Assessment for Web Application
p.1323

Image-Based Simulation for Snowy Scenes
p.1329

A Primary Study on the Relationship between Internet Involvement and Adolescents’ Mental Health
p.1336

Thermal Wave Image Reconstruction of Bonding Defects in Missile Engine Shell
p.1344

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 58-60An Approach to Expert Finding Based on...

An Approach to Expert Finding Based on Multi-Granularity Two-Tuple Linguistic Information

Abstract:

Since organizational tacit knowledge resides in the owner’s brain, finding relevant experts in the specific fields is crucial to utilize this type of knowledge. In this paper, an approach to expert finding is proposed to assist the user to find the required experts. The method adopts the multi-granularity two-tuple linguistic information to construct the expert profile, that is, to model expert’s expertise. The user query the expert based on the fuzzy linguistic information. Then, the relevant experts are ranked according to the matching degree between the expert profile and the query. Finally, a numerical example is given to illustrate the effectiveness of the proposed approach.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 58-60)

Pages:

1317-1322

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.58-60.1317

Citation:

Cite this paper

Online since:

June 2011

Authors:

Ming Li

Keywords:

Expert Finding, Fuzzy Linguistic Modeling, Knowledge Management System

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Nonaka, I. & Takeuchi, H: The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. (Oxford University Press, New York 1995).

Google Scholar

[2] Kreiner, K. Tacit knowledge management: the role of artifacts. Journal of Knowledge Management, 6(2) (2002), p.112–123.

DOI: 10.1108/13673270210424648

Google Scholar

[3] Yang, K.W., & Huh, S.Y. Automatic expert identification using a text categorization technique in knowledge management systems. Expert Systems with Applications, 34(2) (2008), pp.1445-1455.

DOI: 10.1016/j.eswa.2007.01.010

Google Scholar

[4] Herrera, F., & Martínez, L. A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6) (2000), p.746– 752.

DOI: 10.1109/91.890332

Google Scholar

[5] Herrera-Viedma, E., & López-Herrera, A. G. A model of information retrieval system with unbalanced fuzzy linguistic information. International Journal of Intelligent Systems, 22(11) (2007), p.1197–1214.

DOI: 10.1002/int.20244

Google Scholar

[6] Herrera-Viedma, E., López-Herrera, A. G., Luque, M., & Porcel, C. A fuzzy linguistic irs model based on a 2-tuple fuzzy linguistic approach. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 15(2) (2007), p.225–250.

DOI: 10.1142/s0218488507004534

Google Scholar