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. Processes to aggre- gate these disparate logs and trigger alerts when particular events occur are often automated today.
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. In this paper, we study the effect of simplifying the resources’ geometries on runtime and F-measure of link discovery approaches.
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 second maps to entities which are depicted by visually similar images and which exist in the conceptual scope of the dataset within which further personalisation tasks are conducted.