Public and expert collaborative evaluation model and algorithm for enterprise knowledge

Abstract

Knowledge is becoming the most important resource for more and more enterprises and increases exponentially, but there is not an effective method to evaluate them cogently. Based on Web2.0, this article firstly builds an enterprise knowledge sharing model. Synthetically taking the advantage of the convenience and low cost in public evaluation and of the specialty in peer review, a public and expert collaborative evaluation (PECE) model and algorithm for enterprise knowledge are put forward. Through analyzing interaction between user's domain weights and scores of knowledge points, the PECE model and algorithm serve to recognise valuable knowledge and domain experts efficiently and therefore improve ordering and utilisation of knowledge. This article also studies malicious and casual evaluation from users and a method is proposed to update user's domain weights. Finally, a case of knowledge sharing system for amanufacturing enterprise is developed and realised. User's behaviour of publishing and evaluating knowledge is simulated and then analysed to verify the feasibility of PECE algorithm based on the system.

Keywords:

• word
• knowledge evaluation
• public and expert collaborative evaluation (PECE)
• knowledge domain
• public evaluation
• peer review
• Web 2.0

Acknowledgements

This work is supported by the National Natural Science Foundation of PR China (Nos. 51175463, 60974083, 71132007 and 70962008), Natural Science Foundation of Zhejiang Province of PR China (Y1110414), and the Tech Support Program of Jiangxi Province of PR China (2009BGB03100).

Notes

1. Document: refers to a ‘virtual’ document in electronic (digital) format. Accepted types of document for specific applications in various fields: Academia: thesis, dissertation, paper, journal; Business and accounting: invoice, quote, RFP, proposal, contract; Law and politics: summons, certificate, license, gazette; Government and industry: white paper; Media and marketing: brief, mock-up, script (Wikipedia 2011).