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        <title>Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases</title>
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        <namePart type="given">Chuxu</namePart>
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    <name type="personal">
        <namePart type="given">Nitesh</namePart>
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    <abstract>Multi-hop relation reasoning over knowledge base is to generate effective and interpretable relation prediction through reasoning paths. The current methods usually require sufficient training data (fact triples) for each query relation, impairing their performances over few-shot relations (with limited triples) which are common in knowledge base. To this end, we propose FIRE, a novel few-shot multi-hop relation learning model. FIRE applies reinforcement learning to model the sequential steps of multi-hop reasoning, besides performs heterogeneous structure encoding and knowledge-aware search space pruning. The meta-learning technique is employed to optimize model parameters that could quickly adapt to few-shot relations. Empirical study on two datasets demonstrate that FIRE outperforms state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.51</identifier>
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%0 Conference Proceedings
%T Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases
%A Zhang, Chuxu
%A Yu, Lu
%A Saebi, Mandana
%A Jiang, Meng
%A Chawla, Nitesh
%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 zhang-etal-2020-shot
%X Multi-hop relation reasoning over knowledge base is to generate effective and interpretable relation prediction through reasoning paths. The current methods usually require sufficient training data (fact triples) for each query relation, impairing their performances over few-shot relations (with limited triples) which are common in knowledge base. To this end, we propose FIRE, a novel few-shot multi-hop relation learning model. FIRE applies reinforcement learning to model the sequential steps of multi-hop reasoning, besides performs heterogeneous structure encoding and knowledge-aware search space pruning. The meta-learning technique is employed to optimize model parameters that could quickly adapt to few-shot relations. Empirical study on two datasets demonstrate that FIRE outperforms state-of-the-art methods.
%R 10.18653/v1/2020.findings-emnlp.51
%U https://aclanthology.org/2020.findings-emnlp.51
%U https://doi.org/10.18653/v1/2020.findings-emnlp.51
%P 580-585

Markdown (Informal)

[Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases](https://aclanthology.org/2020.findings-emnlp.51) (Zhang et al., Findings 2020)

• Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases (Zhang et al., Findings 2020)

ACL

• Chuxu Zhang, Lu Yu, Mandana Saebi, Meng Jiang, and Nitesh Chawla. 2020. Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 580–585, Online. Association for Computational Linguistics.