General Context-Aware Data Matching and Merging Framework

TL;DR

This paper presents a comprehensive, domain-independent framework for data matching and merging that incorporates multiple context dimensions, semantic enrichment, and trust metrics, validated across diverse datasets.

Contribution

The paper introduces a novel, general framework for data matching and merging that considers multiple context types and includes new metrics for framework management.

Findings

Framework achieves improved results across five diverse datasets

Introduction of new attribute, relationship, semantic, and trust metrics

Framework demonstrates domain independence and robustness

Abstract

Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly review and rigorously evaluate a general framework for data matching and merging. The framework employs collective entity resolution and redundancy elimination using three dimensions of context types. In order to achieve domain independent results, data is enriched with semantics and trust. However, the main contribution of the paper is evaluation on five public domain-incompatible datasets. Furthermore, we introduce additional attribute, relationship, semantic and trust metrics, which allow complete framework management. Besides overall results improvement within the framework, metrics could be of independent interest.