Abstract Data Mining and Knowledge Discovery in Databases (KDD) is a research field
concerned with deriving higher-level insights from data. The tasks performed in that field are …

In the recent years, different Web knowledge graphs, both free and commercial, have been
created. While Google coined the term “Knowledge Graph” in 2012, there are also a few …

Abstract Linked Open Data has been recognized as a valuable source for background
information in data mining. However, most data mining tools require features in propositional …

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 …

Lots of data from different domains are published as Linked Open Data (LOD). While there
are quite a few browsers for such data, as well as intelligent tools for particular purposes, a …

Abstract Knowledge Graph Embeddings, ie, projections of entities and relations to lower
dimensional spaces, have been proposed for two purposes:(1) providing an encoding for …

Knowledge Graphs have been recognized as a valuable source for background information
in many data mining, information retrieval, natural language processing, and knowledge …

Clustering entities over knowledge graphs (KGs) is an asset for explorative search and
knowledge discovery. KG embeddings have been intensively investigated, mostly for KG …

For many machine-learning tasks, augmenting the data table at hand with features built from
external sources is key to improving performance. For instance, estimating housing prices …

In this paper we introduce a framework for learning from RDF data using graph kernels that
count substructures in RDF graphs, which systematically covers most of the existing kernels …