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

Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum


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.
Anthology ID:
2020.findings-emnlp.427
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4752–4765
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.427
DOI:
10.18653/v1/2020.findings-emnlp.427
Bibkey:
Cite (ACL):
Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, and Andrew McCallum. 2020. Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4752–4765, Online. Association for Computational Linguistics.
Cite (Informal):
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion (Das et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.427.pdf
Video:
 https://slideslive.com/38940133
Code
 ameyagodbole/Prob-CBR
Data
FB122NELL-995WN18WN18RR