Research & Innovation

Knowledge graphs are labeled and directed multi-graphs that encode information in the form of entities and relationships. They are gaining attention in different areas of computer science: from the improvement of search engines to the development of virtual personal assistants.

Recommender systems are becoming must-have facilities on e-commerce websites to alleviate information overload and to improve user experience. One important component of such systems is the explanations of the recommendations.

In this paper, we explore the synergy between knowledge graph technologies and computer vision tools for personalisation systems. We propose two image user profiling approaches which map an image to knowledge graph entities representing the interests of a user who appreciates the image. The first one maps an image to entities which correspond to the objects appearing in the image.

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.