SciGraph is a Linked Open Data graph published by Springer Nature which contains information about conferences and conference publications. In this paper, we discuss how this dataset can be utilized to build a conference recommendation system, yielding a recall@10 of up to 0.665, and a MAP of up to 0.540, generating recommendations based on authors, abstracts, and keywords. Furthermore, we show how the dataset can be linked to WikiCFP to recommend upcoming conferences.
The future waves of COVID 19 infections will continue to raise serious problems in patients with severe forms of the disease. Bacterial infections associated with SARS-CoV-2 disease may complicate the progress of hospitalized patients with COVID-19. The present study aimed to evaluate the etiological spectrum of superinfection in adult patients with COVID-19 and to investigate the correlation between superinfection with multidrug-resistant (MDR) bacteria and serum procalcitonin (PCT). A total of 82 COVID-19 hospitalized patients with COVID-19 and bacterial superinfection were included. The superinfections were classified into early infections (3-7 days from admission) and late infections (>7 days from admission). Bacterial superinfection etiological spectrum, MDR bacteria profile and levels of serum PCT were studied. The most frequently isolated bacteria were Klebsiella pneumoniae, Acinetobacter baumannii and Enterococcus spp. MDR bacteria were involved in 73.17% of COVID-19 patients with bacterial superinfections. Most MDR bacteria superinfections (73.52%) occurred in the late infection period. Klebsiella pneumoniae, Enterococcus spp. and Methicillin-resistant Staphylococcus aureus were the most common MDR bacteria identified in late infections after hospitalization in 20.43, 4.30 and 4.30% of all infections, respectively. Serum PCT values were significantly higher in patients with MDR bacteria superinfection compared with patients with sensitive bacteria superinfection (P= 0.009). The principal findings of the present study were the high prevalence of superinfection with MDR bacteria among the COVID-19 patients with bacterial superinfections and the presence of a statistically significant association between serum PCT levels and the presence of superinfection with MDR bacteria. The most effective way to fight against microbial resistance to antibiotics, whether it occurs independently or overlaps with viral infections, is to pursue a national policy for the rational use of antibiotics.
In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community – Semantic Web Machine Learning (SWeML for short). Due to its rapid growth and impact on several communities in the last two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the last decade in this area, where we focused on evaluating architectural, and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this paper is a classification system for SWeML Systems which we publish as ontology.
News consumption has shifted over time from traditional media to online platforms, which use recommendation algorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their preferences and reading behavior. In comparison to domains such as movies or e-commerce, where recommender systems have proved highly successful, the characteristics of the news domain (e.g., high frequency of articles appearing and becoming outdated, greater dynamics of user interest, less explicit relations between articles, and lack of explicit user feedback) pose additional challenges for the recommendation models. While some of these can be overcome by conventional recommendation techniques, injecting external knowledge into news recommender systems has been proposed in order to enhance recommendations by capturing information and patterns not contained in the text and metadata of articles, and hence, tackle shortcomings of traditional models. This survey provides a comprehensive review of knowledge-aware news recommender systems. We propose a taxonomy that divides the models into three categories: neural methods, non-neural entity-centric methods, and non-neural path-based methods. Moreover, the underlying recommendation algorithms, as well as their evaluations are analyzed. Lastly, open issues in the domain of knowledge-aware news recommendations are identified and potential research directions are proposed.
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