Graph Kernels for RDF Data

Abstract

Abstract. The increasing availability of structured data in Resource Description Framework (RDF) format poses new challenges and opportunities for data mining. Existing approaches to mining RDF have only focused on one specific data representation, one specific machine learning algorithm or one specific task. Kernels, however, promise a more flexible approach by providing a powerful framework for decoupling the data representation from the learning task. This paper focuses on how the well established family of kernel-based machine learning algorithms can be readily applied to instances represented as RDF graphs. We first review the problems that arise when conventional graph kernels are used for RDF graphs. We then introduce two versatile families of graph kernels specifically suited for RDF, based on intersection graphs and intersection trees. The flexibility of the approach is demonstrated on two common relational learning tasks: entity classification and link prediction. The results show that our novel RDF graph kernels used with Support Vector Machines (SVMs) achieve competitive predictive performance when compared to specialized techniques for both tasks.

Entity-Based Data Source Contextualization for Searching the Web of Data

“…Note, [16] introduced further kernels, however, we found path kernels to be simple and perform well in our experiments, cf. Sect.…”

“…4. In particular, we made use of three kernels for capturing entity similarity: a path kernel, a substring kernel, and a numerical kernel [16,19]. As baselines we used two approaches: a schema-based contextualization SC [2] and a keyword-based contextualization KC.…”

“…With regard to the former, we measure structural "overlaps" between entity descriptions, G e and G e , using graph intersections [16]:…”

LODifier: Generating Linked Data from Unstructured Text

“…Regarding structural similarity measures, we have only scratched the surface of the possibilities. More involved graph matching procedures remain a challenge due to efficiency reasons; however, this is an area of active research [17].…”

RDF2Vec: RDF Graph Embeddings for Data Mining

“…Only a few approaches are general enough to be applied on any given RDF data, regardless the data mining task. Lösch et al [12] introduce two general RDF graph kernels, based on intersection graphs and intersection trees. Later, the intersection tree path kernel was simplified by Vries et al [33].…”