Abstract Knowledge graph embeddings that generate vector space representations of
knowledge graph triples, have gained considerable popularity in past years. Several …

Artificial Intelligence applications gradually move outside the safe walls of research labs and
invade our daily lives. This is also true for Machine Learning methods on Knowledge …

With the rapid growth of interaction data, many clustering methods have been proposed to
discover interaction patterns as prior knowledge beneficial to downstream tasks …

Completion of large-scale knowledge bases, such as DBPedia or Freebase, often relies on
embedding models that turn symbolic relations into vector-based representations. Such …

In this paper, we study bias in knowledge graph embeddings. We focus on gender bias in
occupations, but our approach is applicable to other types of bias. We start by proposing …

Rapid development of autonomous vehicles has enabled the collection of huge amounts of
multimodal road traffic data resulting in large knowledge graphs for autonomous driving …

Pattern discovery in multidimensional data sets has been the subject of research for
decades. There exists a wide spectrum of clustering algorithms that can be used for this …

At the heart of smart manufacturing is real-time semi-automatic decision-making. Such
decisions are vital for optimizing production lines, eg, reducing resource consumption …

We study the problem of explainability-first clustering where explainability becomes a first-
class citizen for clustering. Previous clustering approaches use decision trees for …

We propose approxSemanticCrossE, an approach for generating explanations to link
prediction problems on Knowledge Graphs. Due to their incompleteness, several models …