OEKG: The Open Event Knowledge Graph

Accessing and understanding contemporary and historical events of global impact such as the US elections and the Olympic Games is a major prerequisite for cross-lingual event analytics that investigate event causes, perception and consequences across country borders. In this paper, we present the Open Event Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph composed of seven different data sets from multiple application domains, including question answering, entity recommendation and named entity recognition. These data sets are all integrated through an easy-to-use and robust pipeline and by linking to the event-centric knowledge graph EventKG. We describe their common schema and demonstrate the use of the OEKG at the example of three use cases: type-specific image retrieval, hybrid question answering over knowledge graphs and news articles, as well as language-specific event recommendation. The OEKG and its query endpoint are publicly available

The Promise and Challenge of Large Language Models for Knowledge Engineering:Insights from a Hackathon

Knowledge engineering (KE) is the process of managing knowledge in a machine-readable way. This often takes the form of Knowledge Graphs (KGs). The advent of new technologies like Large Language Models (LLMs), besides enhancing automated processes in KG construction, has also changed KE work. We conducted a multiple-methods study exploring user opinions and needs regarding the use of LLMs in KE. We used ethnographic techniques to observe KE workers using LLMs to solve KE tasks during a hackathon, followed by interviews with some of the participants. This interim study found that despite LLMs' promising capabilities for efficient knowledge acquisition and multimodality, their effective deployment requires an extended set of capabilities and training, particularly in prompting and understanding data. LLMs can be useful for simple quality assessment tasks, but in complex scenarios, the output cannot be controlled and evaluation may require novel approaches. With this study, we aim to support with evidence the interaction of KE stakeholders with LLMs, identify areas of potential and understand the barriers to their effective use. Copilot approaches may be valuable in developing processes where the human or a team of humans is assisted by generative AI

Fetal Heart Sounds Detection Using Wavelet Transform and Fractal Dimension

Phonocardiography is a non-invasive technique for the detection of fetal heart sounds (fHSs). In this study, analysis of fetal phonocardiograph (fPCG) signals, in order to achieve fetal heartbeat segmentation, is proposed. The proposed approach (namely WT–FD) is a wavelet transform (WT)-based method that combines fractal dimension (FD) analysis in the WT domain for the extraction of fHSs from the underlying noise. Its adoption in this field stems from its successful use in the fields of lung and bowel sounds de-noising analysis. The efficiency of the WT–FD method in fHS extraction has been evaluated with 19 simulated fHS signals, created for the present study, with additive noise up to (3 dB), along with the simulated fPCGs database available at PhysioBank. Results have shown promising performance in the identification of the correct location and morphology of the fHSs, reaching an overall accuracy of 89% justifying the efficacy of the method. The WT–FD approach effectively extracts the fHS signals from the noisy background, paving the way for testing it in real fHSs and clearly contributing to better evaluation of the fetal heart functionality

Driving pressure during proportional assist ventilation: an observational study

Abstract Background During passive mechanical ventilation, the driving pressure of the respiratory system is an important mediator of ventilator-induced lung injury. Monitoring of driving pressure during assisted ventilation, similar to controlled ventilation, could be a tool to identify patients at risk of ventilator-induced lung injury. The aim of this study was to describe driving pressure over time and to identify whether and when high driving pressure occurs in critically ill patients during assisted ventilation. Methods Sixty-two patients fulfilling criteria for assisted ventilation were prospectively studied. Patients were included when the treating physician selected proportional assist ventilation (PAV+), a mode that estimates respiratory system compliance. In these patients, continuous recordings of all ventilator parameters were obtained for up to 72 h. Driving pressure was calculated as tidal volume-to-respiratory system compliance ratio. The distribution of driving pressure and tidal volume values over time was examined, and periods of sustained high driving pressure (≥ 15 cmH2O) and of stable compliance were identified and analyzed. Results The analysis included 3200 h of ventilation, consisting of 8.8 million samples. For most (95%) of the time, driving pressure was < 15 cmH2O and tidal volume < 11 mL/kg (of ideal body weight). In most patients, high driving pressure was observed for short periods of time (median 2.5 min). Prolonged periods of high driving pressure were observed in five patients (8%). During the 661 periods of stable compliance, high driving pressure combined with a tidal volume ≥ 8 mL/kg was observed only in 11 cases (1.6%) pertaining to four patients. High driving pressure occurred almost exclusively when respiratory system compliance was low, and compliance above 30 mL/cmH2O excluded the presence of high driving pressure with 90% sensitivity and specificity. Conclusions In critically ill patients fulfilling criteria for assisted ventilation, and ventilated in PAV+ mode, sustained high driving pressure occurred in a small, yet not negligible number of patients. The presence of sustained high driving pressure was not associated with high tidal volume, but occurred almost exclusively when compliance was below 30 mL/cmH2O