Public and expert collaborative evaluation model and algorithm for enterprise knowledge

Chengyi Le, Xinjian Gu, Kai Pan, Feng Dai, Guoning Qi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)375-393
Number of pages19
JournalEnterprise Information Systems
Volume7
Issue number3
DOIs
Publication statusPublished - 1 Aug 2013
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management

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