Clink - A Novel Record Linkage Methodology based on Graph Interactions

Mahmoud Boghdady, Neamat El-Tazi

2017

Abstract

With the advent of the big-data era and the rapid growth of the amount of data, companies are faced with more opportunities and challenges to outperform their peers, innovate, compete, and capture value from big-data platforms such as social networks. Utilizing the full benefit of social media requires companies to identify their own customers against customers as a whole by linking their local data against data from social media applying record-linkage techniques that differ from simple to complex. For large sources that have huge data and fewer constraints over data, the linking process produces low quality results and requires a lot of pairwise comparisons. We propose a study on how to calculate similarity score not only based on string similarity techniques or topological graph similarity, but also using graph interactions between nodes to effectively achieve better linkage results.

Paper Citation

in Harvard Style

Boghdady M. and El-Tazi N. (2017). Clink - A Novel Record Linkage Methodology based on Graph Interactions . In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-255-4, pages 165-171. DOI: 10.5220/0006416001650171

in Bibtex Style

@conference{data17,
author={Mahmoud Boghdady and Neamat El-Tazi},
title={Clink - A Novel Record Linkage Methodology based on Graph Interactions},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2017},
pages={165-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006416001650171},
isbn={978-989-758-255-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Clink - A Novel Record Linkage Methodology based on Graph Interactions
SN - 978-989-758-255-4
AU - Boghdady M.
AU - El-Tazi N.
PY - 2017
SP - 165
EP - 171
DO - 10.5220/0006416001650171