In emergencies, treatment needs to be fast, accu-rate and patient-specific. For instance, in emergency scenarios, obstacles like treatment environments and medical difficulties can lead to bad outcomes for patients. Additionally, a drastic change of health vitals can force paramedics to shift to a different treatment in the ongoing treatment of the patient in order to save a patient's life. The KIRETT (engl.: 'Artificial intelligence in rescue operations ‘) demonstrator is developed to provide a rescue operator with a wrist-worn device, enabling treatment recommendation (with the help of knowledge graph) with situation detection models to improve the emergency treatment of a patient. This paper aims to provide a qualitative evaluation of the 2-days testing in the KIRETT project with the focus of knowledge graphs, knowledge fusion, and user-experience-design (UX-design).

This paper provides a brief overview about the latest applications in blockchain domain especially the trends and research questions giving an idea about limitations and benefits and in conclusion a perspective for future work. The goal is to develop open topics for research or business usage and discuss a basic idea of a possible future blockchain approach that can be utilized for the process of interchanging knowledge.

Bug reports are essential software artifacts that describe software bugs using natural language. Bug localization tools can help developers to understand the relation between bug reports and a software system. However, most approaches for localizing bugs work with unstructured textual information from the source codes and bug reports. This paper proposes an approach for locating and visualizing bug reports based on class diagrams representing the overall structural design of a software system. Our approach called IdentiBug takes advantage of deep learning techniques to train our bug localization model to predict connections between a bug report and the system’s class diagram. The result is a ranked list of classes from which we extract and rank a list of class diagram excerpts for assisting the developers during bug documentation and localization.

The maintenance and repair of modern vehicles is a challenge for garages, as different causes of faults lead to similar symptoms in the highly complex vehicles these days. Existing processes for fault-diagnosis based on manufacturer service manuals and human experiences are often inadequate and result in high effort and wrong decisions. In addition to these service manuals which provide basic models for e.g., diagnostic terms, primary physical quantities, causal relationships, and plausibilities, nowadays, internet forums offer a comprehensive source of experiences for solutions to these challenges. This paper, therefore, presents methods for the extraction of knowledge from unstructured and informal contributions in internet forums with the goal to synthesize diagnostic graphs from the established knowledge base, which are part of a maintenance software to supports garages in the maintenance of vehicles by suggesting more efficient and target-oriented diagnostic and maintenance actions in real-time.

The visualization and simplification of complex semantically-related knowledge is one of the main challenges in knowledge discovery. In this regard, the knowledge map is a good visualization instrument to represent and provide suitable information with analysis potential. Multidimensional knowledge bases aim to support this objective and store automatically extracted facts and their dimensional relations from textual knowledge resources. In this paper, a dynamic layout structure for knowledge maps based on dimensional information is introduced. The Concept of the Imitation of the Mental Ability of Word Association (CIMAWA) is applied in this approach to create a graphical structure as arrangement of associated information on different levels of textual information.

As financial markets getting faster and more complex, it is difficult for market participants to manage the information overload. Sentiment analysis is a useful text mining method to process textual content and filter the results with analysis methods to relevant and meaningful information. The paper in hand introduces a new method for sentiment analysis in financial markets which combines word associations and lexical resources. Based on stock market news from January 2000 to February 2014 we analyzed documents on different levels. The results are presented and evaluated in this paper.