In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Abstract Linked Open Data has been recognized as a valuable source for background
information in many data mining and information retrieval tasks. However, most of the …

Interference between pharmacological substances can cause serious medical injuries.
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …

Many applications require to learn, mine, analyze and visualize large-scale graphs. These
graphs are often too large to be addressed efficiently using conventional graph processing …

Vector space embeddings have been shown to perform well when using RDF data in data
mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local …

Random walk on graphs has recently gained immense popularity as a tool for graph data
analytics and machine learning. Currently, random walk algorithms are developed as …

Many real-world graphs are temporal in nature, where the temporal information indicates
when a particular edge is changed (eg, edge insertion and deletion). Performing random …

In this paper, we introduce the ALOD2Vec Matcher, an ontology matching tool that exploits a
Web-scale data set, ie, WebIsA-LOD, as external knowledge source. In order to make use of …

As random walk is a powerful tool in many graph processing, mining and learning
applications, this paper proposes an efficient inmemory random walk engine named …

This paper introduces pyRDF2Vec, a Python software package that reimplements the well-
known RDF2Vec algorithm along with several of its extensions. By making the algorithm …