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  • Knowledge graph rule mining via transfer learning

    Author(s)
    Omran, PG
    Wang, Z
    Wang, K
    Griffith University Author(s)
    Wang, Kewen
    Wang, Zhe
    Year published
    2019
    Metadata
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    Abstract
    Mining logical rules from knowledge graphs (KGs) is an important yet challenging task, especially when the relevant data is sparse. Transfer learning is an actively researched area to address the data sparsity issue, where a predictive model is learned for the target domain from that of a similar source domain. In this paper, we propose a novel method for rule learning by employing transfer learning to address the data sparsity issue, in which most relevant source KGs and candidate rules can be automatically selected for transfer. This is achieved by introducing a similarity in terms of embedding representations of entities, ...
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    Mining logical rules from knowledge graphs (KGs) is an important yet challenging task, especially when the relevant data is sparse. Transfer learning is an actively researched area to address the data sparsity issue, where a predictive model is learned for the target domain from that of a similar source domain. In this paper, we propose a novel method for rule learning by employing transfer learning to address the data sparsity issue, in which most relevant source KGs and candidate rules can be automatically selected for transfer. This is achieved by introducing a similarity in terms of embedding representations of entities, relations and rules. Experiments are conducted on some standard KGs. The results show that proposed method is able to learn quality rules even with extremely sparse data and its predictive accuracy outperformed state-of-the-art rule learners (AMIE+ and RLvLR), and link prediction systems (TransE and HOLE).
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    11441
    DOI
    https://doi.org/10.1007/978-3-030-16142-2_38
    Subject
    Artificial intelligence
    Information and computing sciences
    Publication URI
    http://hdl.handle.net/10072/393007
    Collection
    • Conference outputs

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