Stratified Data Integration

TL;DR

This paper introduces a stratified data integration framework that addresses semantic heterogeneity by organizing data into layered representations, enabling independent handling of different heterogeneity types and improving integration across diverse data sources.

Contribution

The paper presents a novel layered representation approach that systematically manages various types of semantic heterogeneity in data integration tasks.

Findings

Effective in multiple pilot case studies

Successfully handles conceptual, language, knowledge, and data heterogeneity

Enhances data integration in industrial applications

Abstract

We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual(where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language(where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge(in the form of a graph where nodes are entity types and links are properties), and data(in the form of a graph of entities populating the previous knowledge graph). This allows us to state the problem of semantic heterogeneity as a problem of Representation Diversity where the different types of heterogeneity, viz. Conceptual, Language, Knowledge, and Data, are uniformly dealt within each single layer, independently from the others. In this paper we describe the proposed stratified representation of data and the process by which data are first transformed into the target representation, then suitably integrated and then, finally, presented to the user in her preferred format. The proposed framework has been evaluated in various pilot case studies and in a number of industrial data integration problems.