Classifying nodes in knowledge graphs is an important task, eg, for predicting missing types
of entities, predicting which molecules cause cancer, or predicting which drugs are …

This systematic review aims to provide a comprehensive analysis of the state of data-to-text
generation research, focusing on identifying research gaps, offering future directions, and …

The task of verbalization of RDF triples has known a growth in popularity due to the rising
ubiquity of Knowledge Bases (KBs). The formalism of RDF triples is a simple and efficient …

With the rising popularity of user-generated genealogical family trees, new genealogical
information systems have been developed. State-of-the-art natural question answering …

Multiple business scenarios require an automated generation of descriptive human-
readable long text from structured input data, where the source is typically a high-resource …

There is a well-known lexical gap between content expressed in the form of natural
language (NL) texts and content stored in an RDF knowledge base (KB). For tasks such as …

An open issue for Semantic Question Answering Systems is bridging the so called lexical
gap, referring to the fact that the vocabulary used by users in framing a question needs to be …

We tackle the problem of learning the description of skills of machines within an Industry 4.0
setting using Class Expression Learning (CEL). CEL deals with learning description logic …

Recently, there has been a big progress in developing artificial deep-learning neural
networks and large-scale knowledge graphs. However, the results in these two research …

Existing knowledge graphs are incomplete. A lot of unstructured documents are hiding
valuable information. But extracting and structuring that information is expensive. To help …