Abstract Knowledge Graphs are an emerging form of knowledge representation. While
Google coined the term Knowledge Graph first and promoted it as a means to improve their …

Expressing machine-interpretable statements in the form of subject-predicate-object triples
is a well-established practice for capturing semantics of structured data. However, the …

Abstract Knowledge graphs (KGs) have become the preferred technology for representing,
sharing and adding knowledge to modern AI applications. While KGs have become a …

In the absence of a central naming authority on the Semantic Web, it is common for different
datasets to refer to the same thing by different IRIs. Whenever multiple names are used to …

Linked Open Data (LOD) is the largest, collaborative, distributed, and publicly-accessible
Knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) …

The number of Knowledge Graphs (KGs) generated with automatic and manual approaches
is constantly growing. For an integrated view and usage, an alignment between these KGs is …

This paper presents an empirical study aiming at understanding the modeling style and the
overall semantic structure of Linked Open Data. We observe how classes, properties and …

In a decentralised knowledge representation system such as the Web of Data, it is common
and indeed desirable for different knowledge graphs to overlap. Whenever multiple names …

Knowledge graphs (KGs) provide information in machine interpretable form. In cases where
multiple KGs are used in the same system, that information needs to be integrated. This is …

In this paper, we tackle the problem of lack of understandability of deep learning systems by
integrating heterogeneous knowledge sources, and in the specific we present how we used …