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  • Scalable Rule Learning via Learning Representation

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    OmranPUB7074.pdf (156.0Kb)
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    Version of Record (VoR)
    Author(s)
    Omran, PG
    Wang, K
    Wang, Z
    Griffith University Author(s)
    Wang, Kewen
    Wang, Zhe
    Year published
    2018
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    Abstract
    We study the problem of learning first-order rules from large Knowledge Graphs (KGs). With recent advancement in information extraction, vast data repositories in the KG format have been obtained such as Freebase and YAGO. However, traditional techniques for rule learning are not scalable for KGs. This paper presents a new approach RLvLR to learning rules from KGs by using the technique of embedding in representation learning together with a new sampling method. Experimental results show that our system outperforms some state-of-the-art systems. Specifically, for massive KGs with hundreds of predicates and over 10M facts, ...
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    We study the problem of learning first-order rules from large Knowledge Graphs (KGs). With recent advancement in information extraction, vast data repositories in the KG format have been obtained such as Freebase and YAGO. However, traditional techniques for rule learning are not scalable for KGs. This paper presents a new approach RLvLR to learning rules from KGs by using the technique of embedding in representation learning together with a new sampling method. Experimental results show that our system outperforms some state-of-the-art systems. Specifically, for massive KGs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems for rule learning in KGs such as AMIE+. We also used the RLvLR-mined rules in an inference module to carry out the link prediction task. In this task, RLvLR outperformed Neural LP, a state-of-the-art link prediction system, in both runtime and accuracy.
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    Conference Title
    IJCAI International Joint Conference on Artificial Intelligence
    Volume
    2018-July
    DOI
    https://doi.org/10.24963/ijcai.2018/297
    Copyright Statement
    © 2018 International Joint Conference on Artificial Intelligence. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the Conference's website for access to the definitive, published version.
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/381473
    Collection
    • Conference outputs

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