Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum · 2020
A case-based reasoning (CBR) system solves a new problem by retrieving 'cases' that are similar to the given problem.If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability.In this paper, we demonstrate that such a system is achievable for reasoning in knowledgebases (KBs).Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB.Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity.The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization.Our model is non-parametric, growing dynamically as new entities and relations are added to the KB.On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches.Furthermore, we demonstrate the effectiveness of our model in an "open-world" setting where new entities arrive in an online fashion, significantly outperforming state-ofthe-art approaches and nearly matching the best offline method.