Everything you always wanted to know about blank nodes (but were afraid to ask)

In this paper we thoroughly cover the issue of blank nodes, which have been defined in RDF as "existential variables". We first introduce the theoretical precedent for existential blank nodes from first order logic and incomplete Information in database theory. We then cover the different (and sometimes incompatible) treatment of blank nodes across the W3C stack of RDF-related standards. We present an empirical survey of the blank nodes present in a large sample of RDF data published on the Web (the BTC-2012 dataset), where we find that 25.7% of unique RDF terms are blank nodes, that 44.9% of documents and 66.2% of domains featured use of at least one blank node, and that aside from one Linked Data domain whose RDF data contains many "blank node cycles", the vast majority of blank nodes form tree structures that are efficient to compute simple entailment over. With respect to the RDF-merge of the full data, we show that 6.1% of blank-nodes are redundant under simple entailment. The vast majority of non-lean cases are isomorphisms resulting from multiple blank nodes with no discriminating information being given within an RDF document or documents being duplicated in multiple Web locations. Although simple entailment is NP-complete and leanness-checking is coNP-complete, in computing this latter result, we demonstrate that in practice, real-world RDF graphs are sufficiently "rich" in ground information for problematic cases to be avoided by non-naive algorithms

PHTLS ® (Prehospital Trauma Life Support) provider courses in Germany – who takes part and what do participants think about prehospital trauma care training?

BACKGROUND: The goal of this study was to examine PHTLS Provider courses in Germany and to proof the assumption that formation of physicians and paramedics in prehospital trauma care can be optimized. METHODS: PHTLS participants were asked to fill out standardized questionnaires during their course preparation and directly after the course. There were some open questions regarding their professional background and closed questions concerning PHTLS itself. Further questions were to be answered on an analog scale in order to quantify subjective impressions of confidence, knowledge and also to describe individual levels of education and training. RESULTS: 247 questionnaires could be analyzed. Physicians noted significant (p < 0.001) more deficits in their professional training than paramedics. 80% of the paramedics affirmed to have had adequate training with respect to prehospital trauma care, all physicians claimed not to have had sufficient training for prehospital trauma care situations at Medical School. Physicians were statistically most significant dissatisfied then paramedics (p < 0.001). While most participants gave positive feedback, anesthetists were less convinced of PHTLS (p = 0.005), didn’t benefit as much as the rest (p = 0.004) and stated more often, that the course was of less value for their daily work (p = 0.03). After the course confidence increased remarkably and reached higher rates than before the course (p < 0.001). After PHTLS both groups showed similar ratings concerning the course concept indicating that PHTLS could equalize some training deficits and help to gain confidence and assurance in prehospital trauma situations. 90% of the paramedics and 100% of the physicians would recommend PHTLS. Physicians and especially anesthetists revised their opinions with regard to providing PHTLS at Medical School after having taken part in a PHTLS course. CONCLUSION: The evaluation of PHTLS courses in Germany indicates the necessity for special prehospital trauma care training. Paramedics and physicians criticize deficits in their professional training, which can be compensated by PHTLS. With respect to relevant items like confidence and knowledge PHTLS leads to a statistically significant increase in ratings on a visual analogue scale. PHTLS should be integrated into the curriculum at Medical School

Knowledge Graph Embedding for Ecotoxicological Effect Prediction

Exploring the effects a chemical compound has on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. In this paper we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity. The publicly available effect data is integrated to the knowledge graph using ontology alignment techniques. Our experimental results show that the knowledge graph based approach improves the selected baselines