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DOI: 10.14569/IJACSA.2018.090117
PDF

Quality Ranking Algorithms for Knowledge Objects in Knowledge Management Systems

Author 1: Amal Al-Rasheed
Author 2: Jawad Berri

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 1, 2018.

  • Abstract and Keywords
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Abstract: The emergence of web-based Knowledge Management Systems (KMS) has raised several concerns about the quality of Knowledge Objects (KO), which are the building blocks of knowledge expertise. Web-based KMSs offer large knowledge repositories with millions of resources added by experts or uploaded by users, and their content must be assessed for accuracy and relevance. To improve the efficiency of ranking KOs, two models are proposed for KO evaluation. Both models are based on user interactions and exploit user reputation as an important factor in quality estimation. For the purpose of evaluating the performance of the two proposed models, the algorithms were implemented and incorporated in a KMS. The results of the experiment indicate that the two models are comparable in accuracy, and that the algorithms can be integrated in the search engine of a KMS to estimate the quality of KOs and accordingly rank the results of user searches.

Keywords: Knowledge Management System (KMS); Knowledge Object (KO); knowledge evaluation; quality indicator; recommender system

Amal Al-Rasheed and Jawad Berri, “Quality Ranking Algorithms for Knowledge Objects in Knowledge Management Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090117

@article{Al-Rasheed2018,
title = {Quality Ranking Algorithms for Knowledge Objects in Knowledge Management Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090117},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090117},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {1},
author = {Amal Al-Rasheed and Jawad Berri}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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