Research & Innovation

The runtime optimization of federated SPARQL query engines is of central importance to ensure the usability of the Web of Data in real-world applications. The efficient selection of sources (SPARQL endpoints in our case) as well as the generation of optimized query plans belong to the most important optimization steps in this respect.

The way how research is communicated using text publications has not changed much over the past decades. We have the vision that ultimately researchers will work on a common structured knowledge base comprising comprehens- ive semantic and machine-comprehensible descriptions of their research, thus making research contributions more transparent and comparable.

Link discovery is central to the integration and use of data across RDF knowledge bases. Geospatial information are increasingly represented according to the Linked Data principles. Resources within such datasets are described by means of vector geometry, where link discovery approaches have to deal with millions of point sets consisting of billions of points.

The proliferation of ontologies and multilingual data available on the Web has motivated many researchers to con- tribute to multilingual and cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates ontology learning from multilingual text/ontologies in order to support collaborative ontology engineering process.

The existence of many digital libraries, maintained by different organizations, brings challenges to the discoverability of cultural heritage (CH) resources. Metadata aggregation is an approach where centralized efforts like Europeana facilitate their discoverability by collecting the resource’s metadata.

Due to the growing complexity of information systems and the increasing prevalence and sophistication of threats, security man- agement has become an enormously challenging task. To identify suspicious activities, security analysts need to monitor their systems constantly, which involves coping with high volumes of heterogeneous log data from various sources.

Automatic Term Extraction is a fundamental Natural Language Processing task often used in many knowledge acquisition pro- cesses. It is a challenging NLP task due to its high domain dependence: no existing methods can consistently outperform others in all domains, and good ATE is very much an unsolved problem.

The schema.org initiative led by the four major search engines curates a vocabulary for describing web content. The number of semantic annotations on the web are increasing, mostly due to the industrial incentives provided by those search engines. The annotations are not only consumed by search engines, but also by other automated agents like intelligent personal assistants (IPAs).

One of the most critical challenges in human-robot collaborative work settings is ensuring the health and safety of the involved human workers.

Autonomous virtual agents that operate in complex IoT environ- ments face two fundamental challenges: (i) they usually lack su - cient start-up knowledge and (ii) hence are incapable to adequately adjust their internal knowledge base and decision-making policies during runtime to meet speci c user requirements and preferences.