Research

Research Topics

Data Spaces

Establishing a data exchange infrastructure in accordance with pertinent industry standards, with a focus on enabling data sovereignty, fostering the digital economy, promoting interoperability, and building trust.

Information Extraction and Ontology Learning

This line of work looks at the task of extracting various information from textual input. By identifying entities and the relations between them, it is possible to derive a structured ontology representation from these unstructured documents.

Knowledge Infused Information Retrieval 3

Knowledge Graphs and (Generative) Language Models

Our objective is to integrate external taxonomies, ontologies, or knowledge graphs into language models to develop hybrid architectures that exploit the strengths of both technologies, effectively addressing their limitations for complex tasks.

SWeMLS Description Framework

The research seeks to create a structured framework for documenting and visualising the essential elements “SWeML” systems, i.e., hybrid AI systems that incorporate both Semantic Web Technologies and Machine Learning.

NLP Service Orchestration

We present our research on the development, deployment, and usage of collections of interdependent natural language processing services. We aim at the best quality of processing and robustness of the deployed services.

Knowledge Graph enhanced Named Entity Recognition

In this area of research, we are investigating approaches to address the Named Entity Recognition (NER) task as well as how Knowledge Graphs can be used to improve upon and circumvent the shortcomings of existing models.

Word Sense Disambiguation, Target Sense Verification, and Entity Linking

This line of work looks into challenges related to recognizing words in a text as specific entities, understanding their types and linking them to a knowledge graph.