Representation Heterogeneity

TL;DR

This paper introduces a nuanced view of semantic heterogeneity by emphasizing the importance of both representation unity and diversity across language and knowledge layers, advancing understanding of how to manage variance in data representations.

Contribution

It proposes a formal framework for representation heterogeneity based on the concepts of unity and diversity, extending traditional views on semantic variance.

Findings

Defines representation unity and diversity as key to understanding heterogeneity

Highlights how these notions manifest across language and knowledge layers

Provides a conceptual foundation for managing semantic heterogeneity

Abstract

Semantic Heterogeneity is conventionally understood as the existence of variance in the representation of a target reality when modelled, by independent parties, in different databases, schemas and/ or data. We argue that the mere encoding of variance, while being necessary, is not sufficient enough to deal with the problem of representational heterogeneity, given that it is also necessary to encode the unifying basis on which such variance is manifested. To that end, this paper introduces a notion of Representation Heterogeneity in terms of the co-occurrent notions of Representation Unity and Representation Diversity. We have representation unity when two heterogeneous representations model the same target reality, representation diversity otherwise. In turn, this paper also highlights how these two notions get instantiated across the two layers of any representation, i.e., Language and Knowledge.