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

TL;DR

This paper introduces a probabilistic case-based reasoning system for knowledge graph completion that effectively predicts entity attributes by leveraging similar entities and reasoning paths, excelling in open-world scenarios.

Contribution

It presents a non-parametric, scalable probabilistic model for reasoning in knowledge bases that outperforms rule-based methods and matches state-of-the-art embedding approaches.

Findings

Outperforms rule learning approaches on benchmark datasets

Performs comparably to state-of-the-art embedding methods

Significantly outperforms others in open-world, online scenarios

Abstract

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