Difference between revisions of "Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis"

From Wikipedia Quality
Jump to: navigation, search
(New study: Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis)
 
(wikilinks)
 
Line 1: Line 1:
'''Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis''' - scientific work related to Wikipedia quality published in 2018, written by Heeyeul Kwon, Yongtae Park and Youngjung Geum.
+
'''Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Heeyeul Kwon]], [[Yongtae Park]] and [[Youngjung Geum]].
  
 
== Overview ==
 
== Overview ==
Abstract The generation of new and creative ideas is vital to stimulating innovation. Morphological analysis is one appropriate method given its objective, impersonal, and systematic nature. However, how to build a morphological matrix is a critical problem, especially in the big data era. This research focuses on Wikipedia's case-specific characteristics and well-coordinated knowledge structure and attempts to integrate the platform with morphological analysis. In details, several methodological options are explored to implement Wikipedia data into morphological analysis. Authors then propose a Wikipedia-based approach to the development of morphological matrix, which incorporates the data on table of contents , hyperlinks , and categories . Its feasibility was demonstrated through a case study of drone technology, and its validity and effectiveness was shown based on a comparative analysis with a conventional discussion-based approach. The methodology is expected to be served as an essential supporting tool for generating creative ideas that could spark innovation.
+
Abstract The generation of new and creative ideas is vital to stimulating innovation. Morphological analysis is one appropriate method given its objective, impersonal, and systematic nature. However, how to build a morphological matrix is a critical problem, especially in the big data era. This research focuses on [[Wikipedia]]'s case-specific characteristics and well-coordinated knowledge structure and attempts to integrate the platform with morphological analysis. In details, several methodological options are explored to implement Wikipedia data into morphological analysis. Authors then propose a Wikipedia-based approach to the development of morphological matrix, which incorporates the data on table of contents , hyperlinks , and [[categories]] . Its feasibility was demonstrated through a case study of drone technology, and its validity and effectiveness was shown based on a comparative analysis with a conventional discussion-based approach. The methodology is expected to be served as an essential supporting tool for generating creative ideas that could spark innovation.

Latest revision as of 06:34, 14 June 2019

Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis - scientific work related to Wikipedia quality published in 2018, written by Heeyeul Kwon, Yongtae Park and Youngjung Geum.

Overview

Abstract The generation of new and creative ideas is vital to stimulating innovation. Morphological analysis is one appropriate method given its objective, impersonal, and systematic nature. However, how to build a morphological matrix is a critical problem, especially in the big data era. This research focuses on Wikipedia's case-specific characteristics and well-coordinated knowledge structure and attempts to integrate the platform with morphological analysis. In details, several methodological options are explored to implement Wikipedia data into morphological analysis. Authors then propose a Wikipedia-based approach to the development of morphological matrix, which incorporates the data on table of contents , hyperlinks , and categories . Its feasibility was demonstrated through a case study of drone technology, and its validity and effectiveness was shown based on a comparative analysis with a conventional discussion-based approach. The methodology is expected to be served as an essential supporting tool for generating creative ideas that could spark innovation.