Injecting Semantic Background Knowledge into Neural Networks using Graph Embeddings

The inferences of a machine learning algorithm are naturally limited by the available data. In many real-world applications, the provided internal data is domain-specific and we use external background knowledge to derive or add new features. Semantic networks, like linked open data, provide a largely unused treasure trove of background knowledge. This drives a recent surge of interest in unsupervised methods to automatically extract such semantic background knowledge and inject it into machine learning algorithms. In this work, we describe the general process of extracting knowledge from semantic networks through vector space embeddings. The locations in the vector space then reflect relations in the original semantic network. We perform this extraction for geographic background knowledge and inject it into a neural network for the complicated real-world task of credit-card fraud detection. This improves the performance by 11.2%.