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Computer Science > Machine Learning

arXiv:1904.10281 (cs)
[Submitted on 23 Apr 2019 (v1), last revised 31 Oct 2019 (this
version, v3)]

Title:Quaternion Knowledge Graph Embeddings

Authors:Shuai Zhang, Yi Tay, Lina Yao, Qi Liu
View a PDF of the paper titled Quaternion Knowledge Graph Embeddings,
by Shuai Zhang and Yi Tay and Lina Yao and Qi Liu
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    Abstract:In this work, we move beyond the traditional
    complex-valued representations, introducing more expressive
    hypercomplex representations to model entities and relations for
    knowledge graph embeddings. More specifically, quaternion
    embeddings, hypercomplex-valued embeddings with three imaginary
    components, are utilized to represent entities. Relations are
    modelled as rotations in the quaternion space. The advantages of
    the proposed approach are: (1) Latent inter-dependencies (between
    all components) are aptly captured with Hamilton product,
    encouraging a more compact interaction between entities and
    relations; (2) Quaternions enable expressive rotation in
    four-dimensional space and have more degree of freedom than
    rotation in complex plane; (3) The proposed framework is a
    generalization of ComplEx on hypercomplex space while offering
    better geometrical interpretations, concurrently satisfying the
    key desiderata of relational representation learning (i.e.,
    modeling symmetry, anti-symmetry and inversion). Experimental
    results demonstrate that our method achieves state-of-the-art
    performance on four well-established knowledge graph completion
    benchmarks.

Comments: Accepted by NeurIPS 2019
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL);
          Machine Learning (stat.ML)
Cite as:  arXiv:1904.10281 [cs.LG]
          (or arXiv:1904.10281v3 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.1904.10281
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Shuai Zhang [view email]
[v1] Tue, 23 Apr 2019 12:36:59 UTC (165 KB)
[v2] Sat, 25 May 2019 06:11:16 UTC (191 KB)
[v3] Thu, 31 Oct 2019 12:45:00 UTC (190 KB)
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