@inproceedings{das-etal-2020-probabilistic,
title = "Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion",
author = "Das, Rajarshi and
Godbole, Ameya and
Monath, Nicholas and
Zaheer, Manzil and
McCallum, Andrew",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.427",
doi = "10.18653/v1/2020.findings-emnlp.427",
pages = "4752--4765",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
%A Das, Rajarshi
%A Godbole, Ameya
%A Monath, Nicholas
%A Zaheer, Manzil
%A McCallum, Andrew
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F das-etal-2020-probabilistic
%X 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.
%R 10.18653/v1/2020.findings-emnlp.427
%U https://aclanthology.org/2020.findings-emnlp.427
%U https://doi.org/10.18653/v1/2020.findings-emnlp.427
%P 4752-4765
Markdown (Informal)
[Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion](https://aclanthology.org/2020.findings-emnlp.427) (Das et al., Findings 2020)
ACL