Efficient Ontology Meta-Matching Based on Interpolation Model Assisted Evolutionary Algorithm

Ontology is the kernel technique of the Semantic Web (SW), which models the domain knowledge in a formal and machine-understandable way. To ensure different ontologies’ communications, the cutting-edge technology is to determine the heterogeneous entity mappings through the ontology matching process. During this procedure, it is of utmost importance to integrate different similarity measures to distinguish heterogeneous entity correspondence. The way to find the most appropriate aggregating weights to enhance the ontology alignment’s quality is called ontology meta-matching problem, and recently, Evolutionary Algorithm (EA) has become a great methodology of addressing it. Classic EA-based meta-matching technique evaluates each individual through traversing the reference alignment, which increases the computational complexity and the algorithm’s running time. For overcoming this drawback, an Interpolation Model assisted EA (EA-IM) is proposed, which introduces the IM to predict the fitness value of each newly generated individual. In particular, we first divide the feasible region into several uniform sub-regions using lattice design method, and then precisely evaluate the Interpolating Individuals (INIDs). On this basis, an IM is constructed for each new individual to forecast its fitness value, with the help of its neighborhood. For testing EA-IM’s performance, we use the Ontology Alignment Evaluation Initiative (OAEI) Benchmark in the experiment and the final results show that EA-IM is capable of improving EA’s searching efficiency without sacrificing the solution’s quality, and the alignment’s f-measure values of EA-IM are better than OAEI’s participants.

Gastrointestinal symptoms are associated with severity of coronavirus disease 2019: a systematic review and meta-analysis.

OBJECTIVE: Studies have suggested that coronavirus disease 2019 (COVID-19) appears to be more serious in patients with gastrointestinal symptoms. This meta-analysis was conducted to explore the relationship between gastrointestinal symptoms and the severity of COVID-19. METHODS: We searched PubMed, Web of Science, Science Direct, Embase, and Google Scholar on 16 October 2020, to identify observational studies that provided data on gastrointestinal symptoms and severity of COVID-19. Gastrointestinal symptoms include diarrhea, abdominal pain, nausea, and vomiting. The severe rate and the odds ratio (OR) were pooled. Heterogeneity was assessed using the I2 statistic. RESULTS: A total of 21 studies with 5285 patients were included in this meta-analysis. The severe rate of COVID-19 patients with diarrhea was 41.1% [95% confidence interval (CI): 31.0-51.5%], and the OR of association between diarrhea and severe COVID-19 was 1.41 (95% CI: 1.05-1.89); sensitivity analysis showed that the results for the OR and 95% CI were unstable. For abdominal pain, the severe rate and OR of association with severe COVID-19 were 59.3% (95% CI: 41.3-76.4%) and 2.76 (95% CI: 1.59-4.81), respectively; for nausea, 41.4% (95% CI: 23.2-60.7%) and 0.92 (95% CI: 0.59-1.43), respectively; for vomiting, 51.3% (95% CI: 36.8-65.8%) and 1.68 (95% CI: 0.97-2.92), respectively. CONCLUSION: The severe rate was more than 40% in COVID-19 patients with gastrointestinal symptoms. Abdominal pain was associated with a near 2.8-fold increased risk of severe COVID-19; the relationship between diarrhea and the severity of COVID-19 was regionally different; nausea and vomiting were limited in association with an increased risk of severe COVID-19.

Clinical, laboratory, and imaging features of pediatric COVID-19: A systematic review and meta-analysis.

ABSTRACT: Pediatric cases of coronavirus disease 2019 (COVID-19) have been reported. This meta-analysis was aimed at describing the clinical, laboratory, and imaging characteristics of children with COVID-19 based on published data of pediatric COVID-19 cases.Search of PubMed, Embase, Web of Sciences, Science Direct, and Google Scholar for articles published until December 14, 2020, that described the clinical, laboratory, and imaging features of children with COVID-19. Data were extracted independently by 2 authors. Random-effects meta-analysis models were used to report pooled results.Clinical data from 2874 children with COVID-19 from 37 articles were finally included for quantitative analyses. Fever (48.5%, 95% CI: 41.4%-55.6%) and cough (40.6%, 95% CI: 33.9%-47.5%) were the most common symptoms; asymptomatic infection and severe cases, respectively, accounted for 27.7% (95% CI: 19.7%-36.4%) patients and 1.1% of the 1933 patients included. Laboratory tests showed 5.5% (95% CI: 2.8%-8.9%) of the patients had lymphopenia. The pooled prevalence of leukopenia was 7.3% (95% CI: 3.4%-12.2%), and the C-reactive protein level was high in 14.0% (95% CI: 6.8%-22.8%). Chest computed tomography showed unilateral and bilateral lesions, and ground-glass opacity in 29.4% (95% CI: 24.8%-34.3%) and 24.7% (95% CI: 18.2%-31.6%), and 32.9% (95% CI: 25.3%-40.9%), respectively, and normal in approximately 36.0% (95% CI: 27.7%-44.7%).We found that children with COVID-19 had relatively mild disease, with quite a lot of asymptomatic infections and low rate of severe illness. Data from more regions are needed to determine the prevention and treatment strategies for children with COVID-19.