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

arXiv: https://arxiv.org/abs/2010.03548 · HTML: https://mlsanity.com/paper/2010.03548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Event Prediction using Case-Based Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2309.12423/) (2023)
- [KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models](https://mlsanity.com/paper/2501.02711/) (2025)

## Abstract (arXiv)

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