A Methodology for Empirical Analysis of LOD Datasets

TL;DR

The paper introduces CoCoE, a comprehensive methodology using complexity, coherence, and entropy measures for empirical analysis of Linked Open Data datasets, demonstrated on biomedical RDF data.

Contribution

It presents a novel, extensible framework for evaluating RDF datasets' suitability for various knowledge discovery tasks.

Findings

CoCoE effectively differentiates datasets based on complexity and informativeness.

The methodology provides insights into dataset structure and suitability for specific applications.

Application to biomedical datasets demonstrates practical utility.

Abstract

CoCoE stands for Complexity, Coherence and Entropy, and presents an extensible methodology for empirical analysis of Linked Open Data (i.e., RDF graphs). CoCoE can offer answers to questions like: Is dataset A better than B for knowledge discovery since it is more complex and informative?, Is dataset X better than Y for simple value lookups due its flatter structure?, etc. In order to address such questions, we introduce a set of well-founded measures based on complementary notions from distributional semantics, network analysis and information theory. These measures are part of a specific implementation of the CoCoE methodology that is available for download. Last but not least, we illustrate CoCoE by its application to selected biomedical RDF datasets.