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.

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关于学习上下文感知规则以链接RDF数据集

集成RDF数据集已成为研究人员和从业人员的一个相关问题。在文献中，有许多遗传学建议学习规则，这些规则允许链接引用相同真实世界实体的资源，这对于集成数据集至关重要。不幸的是，它们不了解上下文，因为它们专注于资源及其属性，却忽略了邻居。这意味着，在不同资源具有相似属性但引用不同的现实世界实体的情况下，或它们具有不同属性但引用相同的现实世界实体的情况下，它们就达不到要求。在本文中，我们提出了一项提案，该提案将学习考虑上下文的​​规则，该规则应同时考虑资源及其邻居的属性。我们进行了广泛的实验，证明其性能优于最先进的遗传方案。我们的结论使用统计上合理的方法进行了检验。