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    Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases

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    Type
    Conference Paper
    Authors
    Zhang, Chuxu
    Yu, Lu
    Saebi, Mandana
    Jiang, Meng
    Chawla, Nitesh
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/667823
    
    Metadata
    Show full item record
    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
    Citation
    Zhang, C., Yu, L., Saebi, M., Jiang, M., & Chawla, N. (2020). Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases. Findings of the Association for Computational Linguistics: EMNLP 2020. doi:10.18653/v1/2020.findings-emnlp.51
    Sponsors
    This work was supported in part by National Science Foundation grants CCI-1925607 and IIS1849816. We also thank the anonymous reviewers for their valuable comments and helpful suggestions.
    Publisher
    Association for Computational Linguistics (ACL)
    Conference/Event name
    Association for Computational Linguistics
    DOI
    10.18653/v1/2020.findings-emnlp.51
    Additional Links
    https://www.aclweb.org/anthology/2020.findings-emnlp.51
    ae974a485f413a2113503eed53cd6c53
    10.18653/v1/2020.findings-emnlp.51
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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