Leveraging Graph Neighborhoods for Efficient Inference

Several probabilistic extensions of description logic languages have been proposed and thoroughly studied. However, their practical use has been hampered by intractability of various reasoning tasks. While present-day knowledge bases (KBs) contain millions of instances and thousands of axioms, most state-of-the-art reasoners are capable of handling small scale KBs with thousands of instances. Thus, recent research has focused on leveraging the structure of KBs and queries in order to speed up inference runtime. However, these efforts have not been satisfactory in providing reasoners that are suitable for practical use in large scale KBs. In this study, we aim to tackle this challenging problem. In doing so, we use a probabilistic extension of OWL RL (called PRORL) as a modeling language and exploit graph neighborhoods (of undirected graphical models) for efficient approximate probabilistic inference. We show that sub-graph extraction based inference is much faster and has comparable accuracy to full graph inference. We perform several experiments, in order to support our claim, over a NELL KB containing millions of instances and thousands of axioms. Furthermore, we propose a novel graph-based algorithm to automatically partition inferences rules based on their structure for efficient parallel inference.