On learning context-aware rules to link RDF datasets

Abstract

Integrating RDF datasets has become a relevant problem for both researchers and practitioners. In the literature, there are many genetic proposals that learn rules that allow to link the resources that refer to the same real-world entities, which is paramount to integrating the datasets. Unfortunately, they are context-unaware because they focus on the resources and their attributes but forget about their neighbours. This implies that they fall short in cases in which different resources have similar attributes but refer to different real-world entities or cases in which they have dissimilar attributes but refer to the same real-world entities. In this article, we present a proposal that learns context-aware rules that take into account both the attributes of the resources and their neighbours. We have conducted an extensive experimentation that proves that it outperforms the most advanced genetic proposal. Our conclusions were checked using statistically sound methods.

Helio: A framework for implementing the life cycle of knowledge graphs

“…RDF link discovery aims at producing relationships between local RDF resources and other RDF resources allocated in different KGs [58]. On the one hand, there is a wide number of tools that aim at producing link rules [16][17][18]23,43,44,60,61,75], i.e., restrictions under which two RDF resources are linked. On the other hand, other tools focus on applying those rules efficiently and producing the links among resources [26,59,79].…”

“…As an example, in the repository of plugins 18 it can be found a data handler that allows Helio to fetch data from an Ethereum blockchain. 19 A new user that would like to use this plugin should only download the jar, configure Helio to use it, 20 and define a correct mapping.…”