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Enhancing Data Quality using Human Computation and Crowd Sourcing


Article Information

Title: Enhancing Data Quality using Human Computation and Crowd Sourcing

Authors: Vikram Kumar Kirpalani, Muhammad Ejaz Tayab

Journal: Journal of Independent Studies and Research Computing

HEC Recognition History
Category From To
Y 2024-10-01 2025-12-31
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
Y 2019-12-20 2020-06-30
Z 2018-05-11 2019-12-19

Publisher: Shaheed Zulfikar Ali Bhutto Institute of Sc. & Technology (SZABIST), Karachi

Country: Pakistan

Year: 2015

Volume: 13

Issue: 1

Language: English

DOI: 10.31645/jisrc/(2015).13.1.0010

Keywords: Data QualityHuman ComputationCrowd Sourcing

Categories

Abstract

This paper is aimed at addressing the issues that are present in the data dumps available at DBpedia by using the concept of associations i.e. concept hierarchy to enhance the quality of those data dumps. These data dumps are extracted from Wikipedia and the issues that prevail in these data dumps is because of either the data extraction frameworks or the human error during crowd-sourcing efforts made on Wikipedia. By using Human Computation techniques and employing Crowd sourcing together with query morphing, diving deeper into this subject would become easier in a better way. One of the key issues with the datasets is the presence of multiple values in a single attribute and vice versa especially in the “Place of Birth” field of important personalities. This paper highlights the implementation process in order to solve these issues and adds a survey conducted on Crowd Sourcing to highlight its impact.


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