Wisdom extraction in knowledge-based information systems

Zaki Malik (Texas A&M University-Commerce, Commerce, Texas, USA)
Khayyam Hashmi (Wayne State University, Detroit, Michigan, USA)
Erfan Najmi (Microsoft Corporation, Redmond, Washington, USA)
Abdelmounaam Rezgui (Illinois State University, Normal, Illinois, USA)

Journal of Knowledge Management

ISSN: 1367-3270

Publication date: 14 January 2019

Abstract

Purpose

This paper aims to provide a number of distinct approaches towards this goal, i.e. to translate the information contained in the repositories into knowledge. For centuries, humans have gathered and generated data to study the different phenomena around them. Consequently, there are a variety of information repositories available in many different fields of study. However, the ability to access, integrate and properly interpret the relevant data sets in these repositories has mainly been limited by their ever expanding volumes. The goal of translating the available data to knowledge, eventually leading to wisdom, requires an understanding of the relations, ordering and associations among the data sets.

Design/methodology/approach

While the existing information repositories are rich in content, there are no easy means of understanding the relevance or influence of the different facts contained therein. Therefore, the interest of the general populace in terms of prioritizing some data items (or facts) over others is usually lost. In this paper, the goal is to provide approaches for transforming the available facts in the information repositories to wisdom. The authors target the lack of order in the facts presented in the repositories to create a hierarchical distribution based on the common understanding, expectations, opinions and judgments of the different users.

Findings

The authors present multiple approaches to extract and order the facts related to each concept, using both automatic and semi-automatic methods. The experiments show that the results of these approaches are similar and very close to the instinctive ordering of facts by users.

Originality/value

The authors believe that the work presented in this paper, with some additions, can be a feasible step to convert the available knowledge to wisdom and a step towards the future of online information systems.

Keywords

Citation

Malik, Z., Hashmi, K., Najmi, E. and Rezgui, A. (2019), "Wisdom extraction in knowledge-based information systems", Journal of Knowledge Management, Vol. 23 No. 1, pp. 23-45. https://doi.org/10.1108/JKM-05-2018-0288

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

To read the full version of this content please select one of the options below

You may be able to access this content by logging in via Shibboleth, Open Athens or with your Emerald account.
To rent this content from Deepdyve, please click the button.
If you think you should have access to this content, click the button to contact our support team.