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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于镜像空间的平移嵌入模型

葛学伟 范贵生 虞慧群

葛学伟, 范贵生, 虞慧群. 基于镜像空间的平移嵌入模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20211129001
引用本文: 葛学伟, 范贵生, 虞慧群. 基于镜像空间的平移嵌入模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20211129001
Ge Xuewei, Fan Guisheng, Yu Huiqun. MTransE: Mirrored Translation Embedding Model[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20211129001
Citation: Ge Xuewei, Fan Guisheng, Yu Huiqun. MTransE: Mirrored Translation Embedding Model[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20211129001

基于镜像空间的平移嵌入模型

doi: 10.14135/j.cnki.1006-3080.20211129001
详细信息
  • 中图分类号: TP3

MTransE: Mirrored Translation Embedding Model

  • 摘要: 知识图广泛应用于许多人工智能(AI)任务。然而,现有知识图通常是不完整的,需对知识图进行补全或链接预测。本文通过对知识图中的实体和关系进行嵌入来预测知识图的缺失环节的问题。引入镜像空间的概念,使得模型具有学习对称/反对称模式的能力。在新的空间模型中,关系仍然被建模为平移,而实体被建模为具有镜像点的点。最后,提出的MTransE模型将镜像空间的概念应用到TransE上,并在四个广泛使用的数据集上进行实验,实验结果表明,该方法能减少参数的规模,并提高在4个广泛使用的知识补全数据集上的性能。

     

  • 图  1  对称关系在二维平面上的嵌入示意图

    Figure  1.  A diagram of the embedding of symmetric relations in a two-dimensional space

    图  2  $ [-8\pi ,8\pi ] $中的距离函数图像

    Figure  2.  Distance functions in $ [-8\mathrm{\pi },8\mathrm{\pi }] $

    表  1  各算法打分函数及其复杂度

    Table  1.   The scoring functions of algorithms and their complexities

    ModelScoring FunctionParameters${\mathcal{O} }_{\mathit{{\rm{t}}}\mathit{{\rm{i}}}\mathit{{\rm{m}}}\mathit{{\rm{e}}} }$
    TransE$\left\|\left(\boldsymbol{h}+\boldsymbol{r}\right)-\boldsymbol{t}\right\|$$\boldsymbol{h},\boldsymbol{r},\boldsymbol{t}\in {\mathbb{R} }^{k}$$ \mathcal{O}\left(k\right) $
    HolE$< \mathbf{r},\boldsymbol{h}*\boldsymbol{t} >$$\boldsymbol{h},\boldsymbol{r},\boldsymbol{t}\in {\mathbb{R} }^{k}$$\mathcal{O}\left(k\;\mathrm{lg}\;k\right)$
    DistMult$< \boldsymbol{r},\boldsymbol{h},\boldsymbol{t} >$$\boldsymbol{h},\boldsymbol{r},\boldsymbol{t}\in {\mathbb{R} }^{k}$$ \mathcal{O}\left(k\right) $
    ComplEx$Re( < \boldsymbol{r},\boldsymbol{h},\stackrel{-}{\boldsymbol{t} } > )$$\boldsymbol{h},\boldsymbol{r},\boldsymbol{t}\in {\mathbb{C} }^{k}$$ \mathcal{O}\left(k\right) $
    MtransE$\left\|\boldsymbol{h}+\boldsymbol{r}-{\boldsymbol{t} }^{\mathit{*} }\right\|$$\boldsymbol{h},\boldsymbol{r},\boldsymbol{t},{\boldsymbol{t} }^{\mathit{*} }\in {\mathbb{R} }^{k},$
    $\left(\boldsymbol{t}-{\boldsymbol{t} }^{\mathit{*} }\right)mod\;b={\boldsymbol{0} }$
    $ \mathcal{O}\left(k\right) $
    下载: 导出CSV

    表  2  实验数据集

    Table  2.   Datasets

    Dataset#entity#relation#training#validation#test
    FB15k1495113454831425000059071
    WN18409431814144250005000
    FB15k-237145412372721151753520466
    WN18RR40943118683530343134
    下载: 导出CSV

    表  3  数据集FB15K上的实验结果

    Table  3.   Results on dataset FB15K

    ModelMRMRRHit@1Hit@3Hit@10
    TransE-0.4630.2970.5780.749
    HolE-0.5240.4020.6130.739
    DistMult420.798--0.893
    MTransE420.80.7520.830.885
    下载: 导出CSV

    表  4  数据集FB15K-237上的实验结果

    Table  4.   Results on dataset FB15K-237

    ModelMRMRRHit@1Hit@3Hit@10
    TransE3570.294--0.465
    ComplEx3390.2470.1580.2750.428
    DistMult2540.2410.1550.2630.419
    MTransE2470.3160.2260.3490.5
    下载: 导出CSV

    表  5  数据集WN18上的实验结果

    Table  5.   Results on dataset WN18

    ModelMRMRRHit@1Hit@3Hit@10
    TransE-0.4950.1130.8880.943
    HolE-0.9380.930.9450.949
    DistMult6550.797--0.946
    MTransE3520.9490.9450.9510.957
    下载: 导出CSV

    表  6  数据集WN18RR上的实验结果

    Table  6.   Results on dataset WN18

    ModelMRMRRHit@1Hit@3Hit@10
    TransE33840.226--0.501
    ComplEx52610.440.410.460.51
    DistMult51100.430.390.440.49
    MTransE51830.4540.4240.4630.514
    下载: 导出CSV

    表  7  RotatE和MTransE在FB15K上的实验结果

    Table  7.   Results of RotatE and MTransE on FB15K

    ModelMRMRRHit@1Hit@3Hit@10
    RotatE400.7970.7460.830.884
    MTransE420.80.7520.830.885
    下载: 导出CSV

    表  8  RotatE和MtransE在WN18上的实验结果

    Table  8.   Results of RotatE and MTransE on WN18

    ModelMRMRRHit@1Hit@3Hit@10
    RotatE3090.9490.9440.9520.959
    MTransE3520.9490.9450.9510.957
    下载: 导出CSV
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  • 网络出版日期:  2022-04-12

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