# Stratified Data Integration

arXiv: https://arxiv.org/abs/2105.09432 · HTML: https://mlsanity.com/paper/2105.09432/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 4 of 4)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs](https://mlsanity.com/paper/2008.07176/) (2020)
- [The Knowledge Graph Track at OAEI -- Gold Standards, Baselines, and the Golden Hammer Bias](https://mlsanity.com/paper/2002.10283/) (2020)
- [FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation](https://mlsanity.com/paper/2008.13482/) (2020)

## Built on by (most cited first, 9 of 9)

- [A Diversity-Aware Domain Development Methodology](https://mlsanity.com/paper/2208.13064/) (2022)
- [Representation Heterogeneity](https://mlsanity.com/paper/2207.01091/) (2022)
- [From Knowledge Representation to Knowledge Organization and Back](https://mlsanity.com/paper/2312.07302/) (2023)
- [Popularity Driven Data Integration](https://mlsanity.com/paper/2209.14049/) (2022)
- [Building Interoperable Electronic Health Records as Purpose-Driven Knowledge Graphs](https://mlsanity.com/paper/2305.06088/) (2023)
- [Towards Knowledge Organization Ecosystems](https://mlsanity.com/paper/2105.10923/) (2021)
- [GENOME: A GENeric methodology for Ontological Modelling of Epics](https://mlsanity.com/paper/2202.13751/) (2022)
- [From Knowledge Organization to Knowledge Representation and Back](https://mlsanity.com/paper/2401.11753/) (2024)
- [KoRe: Compact Knowledge Representations for Large Language Models](https://mlsanity.com/paper/2605.20170/) (2026)

## Abstract (arXiv)

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.