Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion

• Published in: arXiv.org
• Main Authors: Das, Rajarshi, Godbole, Ameya, Monath, Nicholas, Manzil Zaheer, McCallum, Andrew
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 09.10.2020
• Subjects: Iterative methods; Knowledge bases (artificial intelligence); Optimization; Probabilistic models; Reasoning
• ISSN: 2331-8422

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 knowledge-bases (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-of-the-art approaches and nearly matching the best offline method. Code available at https://github.com/ameyagodbole/Prob-CBR