A Principled Approach to Bridging the Gap between Graph Data and their Schemas

TL;DR

This paper introduces a formal framework for measuring and refining the structuredness of RDF graphs, addressing incomplete schema conformance by defining rules, and demonstrates its effectiveness on real datasets.

Contribution

It proposes a formal language for specifying structuredness functions, proves the NP-completeness of related refinement problems, and applies ILP to analyze real-world RDF data.

Findings

Rules effectively refine datasets based on structuredness

ILP approach successfully applied to DBpedia and WordNet datasets

Framework enhances understanding of RDF data structure

Abstract

Although RDF graphs have schema information associated with them, in practice it is very common to find cases in which data do not fully conform to their schema. A prominent example of this is DBpedia, which is RDF data extracted from Wikipedia, a publicly editable source of information. In such situations, it becomes interesting to study the structural properties of the actual data, because the schema gives an incomplete description of the organization of a dataset. In this paper we have approached the study of the structuredness of an RDF graph in a principled way: we propose a framework for specifying structuredness functions, which gauge the degree to which an RDF graph conforms to a schema. In particular, we first define a formal language for specifying structuredness functions with expressions we call rules. This language allows a user or a database administrator to state a rule to which an RDF graph may fully or partially conform. Then we consider the issue of discovering a refinement of a sort (type) by partitioning the dataset into subsets whose structuredness is over a specified threshold. In particular, we prove that the natural decision problem associated to this refinement problem is NP-complete, and we provide a natural translation of this problem into Integer Linear Programming (ILP). Finally, we test this ILP solution with two real world datasets, DBpedia Persons and WordNet Nouns, and 4 different and intuitive rules, which gauge the structuredness in different ways. The rules give meaningful refinements of the datasets, showing that our language can be a powerful tool for understanding the structure of RDF data.