Knowledge graphs in machine learning decision optimization

Abstract

Training a machine learning model can include receiving time series data. A knowledge graph structure can be received including nodes and edges, the nodes representing entities associated with the time series data, the edges representing relationships between the nodes connected by the edges. A machine learning model can be structured to forecast a prediction using the time series data. The machine learning model can be structured to integrate the knowledge graph structure as an error term in the machine learning model. The machine learning model can be trained to forecast the prediction based on the time series data and the knowledge graph structure. The error term representing the knowledge graph structure can be regularized for sparsity during training.