Research & Innovation

Images account for an important part of Multimedia Linked Open Data, but currently most of the semantic relations between images and other entities are based on manual semantic annotation.

We developed a quiz game that uses questions automatically generated from the Czech and English DBpedia. The game uses class membership and categorization to split DBpedia resources into domains of questions, such as persons or films. Difficulty of questions is estimated by using the indegree graph metric based on links between Wikipedia pages.

Different access control models are presented in literature for semantic data, allowing the expression and enforcement of access policies that are based on roles and other attributes of the requesting user usually.

There is an emerging demand on efficiently archiving and (temporal) querying different versions of evolving semantic Web data. As novel archiving systems are starting to address this challenge, foundations/standards for benchmarking RDF archives are needed to evaluate its storage space efficiency and the performance of different retrieval operations.

In this paper we present an ontology-driven framework for natural language question analysis and answering over user models (e.g. preferences, habits and health problems of individuals) that are formally captured using ontology design patterns.

Compliance departments in the international finance industry are struggling to use traditional methods to keep up with the demands of new and more stringent regulatory and policy requirements. One initiative supported by many institutions is definition of a common Financial Industry Business Ontology (FIBO).

The process of designing Cyber Physical Production Systems (CPPS), e.g., modern power plants or steel mills, typically takes place in a multi-disciplinary engineering environment, in which experts from various engineering domains and organizations work together towards creating complex engineering artifacts.

Despite recent efforts to achieve a high level of interoperability of Machine Learning (ML) experiments, positively collaborating with the Reproducible Research context, we still run into problems created due to the existence of different ML platforms: each of those have a specific conceptualization or schema for representing data and metadata.

Knowledge graphs encode semantics that describes resources in terms of several aspects, e.g., neighbors, class hierarchies, or node degrees. Assessing relatedness of knowledge graph entities is crucial for several data-driven tasks, e.g., ranking, clustering, or link discovery. However, existing similarity measures consider aspects in isolation when determining entity relatedness.

The Semantic Web provides mechanisms to interlink data in a fast and efficient way and build complex information networks. However, one of the most important features missing for industrial application is version control which allows recording changes and rolling them back at any time if necessary.