Abstract: A machine-learning method to optimize resource allocation extracts human-interpretable (e.g., doctor-interpretable) entity profiles from a text-labeled data graph of a system having a plurality of entities. The data graph is associated with a smart hospital and includes nodes representing the entities and edges between the nodes representing links between the entities. The method includes: obtaining the data graph and extracting the human-interpretable entity profiles from a tensor that has been learned, using a neural network, to capture patterns among text associated with the edges between respective pairs of the nodes. The neural network layers of the neural network are constructed such that the edges are modeled as a function of the associated text and cluster representations of the respective pairs of the nodes.

Abstract: A method extracts human-interpretable entity profiles from a text-labeled data graph of a system having a plurality of entities. The data graph includes nodes representing the entities and edges between the nodes representing links between the entities. A plurality of text is respectively associated with the corresponding edges. The method includes: constructing neural network layers configured such that each of the edges between a pair of the nodes is modeled as a function of the associated text and cluster representations of the pair of the nodes; for each one of the pair of nodes, performing machine learning to learn a tensor to capture patterns among the associated text and the pair of nodes, and extracting the entity profiles from the tensor.

Abstract: A method extracts human-interpretable entity profiles from a text-labeled data graph of a system having a plurality of entities. The data graph includes nodes representing the entities and edges between the nodes representing links between the entities. A plurality of text is respectively associated with the corresponding edges. The method includes: constructing neural network layers configured such that each of the edges between a pair of the nodes is modeled as a function of the associated text and cluster representations of the pair of the nodes; for each one of the pair of nodes, performing machine learning to learn a tensor to capture patterns among the associated text and the pair of nodes, and extracting the entity profiles from the tensor.