Knowledge base completion using distinct subgraph paths

Abstract

Graph feature models facilitate efficient and interpretable predictions of missing links in knowledge bases with network structure (i.e. knowledge graphs). However, existing graph feature models-e.g. Subgraph Feature Extractor (SFE) or its predecessor, Path Ranking Algorithm (PRA) and its variants-depend on a limited set of graph features, connecting paths. This type of features may be missing for many interesting potential links, though, and the existing techniques cannot provide any predictions at all then. In this paper, we address the limitations of existing works by introducing a new graph-based feature model - Distinct Subgraph Paths (DSP). Our model uses a richer set of graph features and therefore can predict new relevant facts that neither SFE, nor PRA or its variants can discover by principle. We use a standard benchmark data set to show that DSP model performs better than the state-of-the-art - SFE (ANYREL) and PRA - in terms of mean average precision (MAP), mean reciprocal rank (MRR) and Hits@5, 10, 20, with no extra computational cost incurred.