Automated Knowledge Base Completion Using Collaborative Filtering and Deep Reinforcement Learning

Abstract

Knowledge-bases (KBs) are usually incomplete due to an exponential increase in the amount of data and its high-order dependency. This fuels a strong demand for KB completion. This paper presents a novel automated KB completion framework that performs the following process cycle: (i) exploring missing factors, (ii) querying the incomplete knowledge, (iii) reasoning on relations between newly discovered factors (iv) and updating the KB. The proposed framework uses the combination of collaborative filtering and deep reinforcement learning. First, it uses memory-based collaborative filtering to infer the missing factors by identifying an head entity and its association with a missing triplet. It then carries out multi-hop relation reasoning using deep reinforcement learning to complete the KB. Simulations on two public datasets demonstrate that our framework successfully completes the KB with high precision without any prior knowledge or additional information.