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Conflict Resolution Using Relation Classification: High-Level Data Fusion in Data Integration

Zeinab Nakhaei¹, Ali Ahmadi², Arash Sharifi¹ and Kambiz Badie³

1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University
   Tehran, Iran
   {zeinab.nakhaei, a.sharifi}@srbiau.ac.ir
2. Faculty of Computer Engineering, K.N. Toosi University of Technology
   Tehran, Iran
   ahmadi@kntu.ac.ir
3. Iran Telecommunication Research Center (ITRC)
   Tehran, Iran
   k_badie@itrc.ac.ir

Abstract

The aim of conflict resolution in data integration systems is to identify the true values from among different and conflicting claims about a single entity provided by different data sources. Most data fusion methods for resolving conflicts between entities are based on two estimated parameters: the truthfulness of data and the trustworthiness of sources. The relations between entities are however an additional source of information that can be used in conflict resolution. In this article, we seek to bridge the gap between two important broad areas, relation estimation and truth discovery, and to demonstrate that there is a natural synergistic relationship between machine learning and data fusion. Specifically, we use relational machine learning methods to estimate the relations between entities, and then use these relations to estimate the true value using some fusion functions. An evaluation of the results shows that our proposed approach outperforms existing conflict resolution techniques, especially where there are few reliable sources.

Key words

conflict resolution, data fusion, relational machine learning, relation estimation, relation classification

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200131014N

Publication information

Volume 18, Issue 3 (June 2021)
Year of Publication: 2021
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Nakhaei, Z., Ahmadi, A., Sharifi, A., Badie, K.: Conflict Resolution Using Relation Classification: High-Level Data Fusion in Data Integration. Computer Science and Information Systems, Vol. 18, No. 3, 1101–1138. (2021), https://doi.org/10.2298/CSIS200131014N