Methods for automated semantic labeling of data are an indispensable basis for increasing the usability of data. On the one hand, they contribute to the homogenization of the annotations and thus to the increase in quality; on the other hand, they reduce the modeling effort, provided that the quality of the used methodology is sufficient. In the past, research has focused primarily on data- and label-based methods. Another approach that has received recent attention is the incorporation of textual data documentations to support the automatic mapping of datasets to a knowledge graph. However, upon deeper analysis, our recent approach called DocSemMap gives away potential in a number of places. In this paper, we extend the current state of the art approach by uncovering existing shortcomings and presenting our own improvements. Using a sequence-to-sequence model (Seq2Seq), we exploit the context of datasets. An additional introduced classifier provides the linkage of documentation and labels for prediction. Our extended approach achieves a sustainable improvement in comparison to the reference approach.

In recent years, Knowledge Graphs and Ontology-based Data Management have proven to be particularly effective in the efficient management and consolidation of heterogeneous data sources. In this context, semantic modeling has proven to be a useful approach for creating semantic data annotations. However, automatically generated semantic models usually need to be revised by a domain expert, who is often not familiar with semantic technologies. For addressing this issue, we propose the PLASMA semantic modeling tool, which aims at enabling domain experts to build semantic models from scratch or refine models created by automatic algorithms. We demonstrate the use of the tool and its user interface in two different semantic data management use cases for integrating smart city data in a public funded project, called City Dataspace, and for creating semantic models in an industrial use case at Siemens AG.

Today, smart city applications are largely based on data collected from different stakeholders. This presupposes that the required data sources are publicly available. While open data platforms already provide a number of urban data sources, enterprises and citizens have few opportunities to make their data available. To complicate things further, if the data is published, the processing of this data is already extremely time-consuming today, as the data sources are heterogeneous and the corresponding homogenization has to be carried out by the data consumers themselves. In this paper, we present a data marketplace that enables different stakeholders (public institutions, enterprises, citizens) to easily provide data that can especially contribute to the further realization of smart cities. This marketplace is based on the principles of semantic data management, i.e., data providers annotate their added data with semantic models. With the help of these models, the data sources can be found and understood by data consumers and finally homogenized in a way that is suitable for their application.

The potential for mHealth solutions is steadily increasing due to an enormous growth in the area of mobile networks and the mobile Internet. However, not only the general connection is becoming faster and more stable, but also the mobile devices themselves are becoming even more advanced. Nowadays, these devices are able to acquire physiological data and transfer them to e.g. a physician or technician to be analyzed before an actual appointment. Using these technological advantages, more and more evidence could be used for diagnosis and treatment. Instead, long preparation and delay are part of everyday practice nowadays and information and data acquisition take up much time before diagnosis.This paper describes a general concept for a centralized screening / pre-diagnosis system for mobile sleep laboratories. The system is designed to have as little influence as possible on the usual sleep environment, but still allows a medically usable recording of sleep activities and health parameters. Furthermore, the concept covers the access possibilities of the attending physician as well as the back-flow of a final diagnosis. Finally, we report on the resulting challenges of such systems with respect to privacy.

Over the last years the number of patients with sleep or sleep-related disorders is continuously growing. Not only sleep laboratories are able to monitor the sleep of patients but also consumer devices like smartphones or fitness trackers allow sleep recording to everyone. A drawback of professional as well as consumer recording is the hardly researched field of comparing similarities within sleep data. This paper presents a novel approach that allows the recognition of similarities between different sleep data sets based on extracted time intervals like sleep stages. The evaluation of the first proof of concept shows its suitability to distinguish between similar and dissimilar sleep data sets. The results open the door for further optimizations of the underlying approach and for further studies e.g. anomaly detection in medical data.

The ever increasing amount of publications in the biomedical domain leads to the challenge of finding the right answer to a specific question within a vast sea of information. For instance, the biomedical search engine PubMed has an index of over 27 million publications. PubMed implements a keyword-based search. While easy to operate, the results are shown as a paginated textual list, which is time-consuming to navigate. To give the users a more convenient way of searching for information and displaying it, a variety of interfaces which operate on top of PubMed have emerged. One of those platforms is ViLiP, developed as visual exploratory interface to PubMed and primarily used in the neuroscience domain. ViLiP presents the result of a user's query in form of an in-situ heat map. In this work, ViLiP is extended by an NLP-based semantic search engine, for the use-case of detecting drug information within a query. Based on linguistic annotations, potential candidates for drug names are selected from an RDF data source, and matching publications are searched for.