Franziska Baessler, Ali Zafar, Anja Ciprianidis, Fabienne Louise Wagner, Sonja Bettina Klein, Sophie Schweizer, Marina Bartolovic, Daniela Roesch-Ely, Beate Ditzen, Christoph Nikendei, Jobst-Hendrik Schultz
Analysis of risk communication teaching in psychosocial and other medical departments
Medical education online
reasoning graphs learning graph data field artificial intelligence recently received much attention machine learning areas graph representation learning graph neural networks graphs also underlying structures interest wide range traditional fields ranging logicoriented knowledge representation reasoning graph kernels statistical relational learning review outline broad map inventory field learning reasoning graphs spans spectrum reasoning form logical deduction learning node embeddings obtain unified perspective diverse landscape introduce simple general semantic concept model covers logic knowledge bases graph neural networks kernel support vector machines many types frameworks still high semantic level survey common strategies model specification using probabilistic factorization standard feature construction techniques based semantic foundation introduce taxonomy reasoning tasks casts problems ranging transductive link prediction asymptotic analysis random graph models queries different complexities given model similarly express learning different frameworks settings terms common statistical maximum likelihood principle overall review aims provide coherent conceptual framework provides basis theoretical analyses respective strengths limitations different approaches handling graph data facilitates combination integration different modeling paradigms