面向链接预测的知识图谱表示模型对比研究<sup>*</sup>

doi:10.11925/infotech.2096-3467.2021.0491

数据分析与知识发现

2021, Vol. 5

Issue (11): 29-44 https://doi.org/10.11925/infotech.2096-3467.2021.0491

研究论文

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面向链接预测的知识图谱表示模型对比研究^*

余传明(

),张贞港,孔令格

中南财经政法大学信息与安全工程学院武汉 430073

Comparing Knowledge Graph Representation Models for Link Prediction

Yu Chuanming(

),Zhang Zhengang,Kong Lingge

School of Information and Safety Engineering, Zhongnan University of Economics and Law,Wuhan 430073, China

摘要
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摘要

【目的】 系统揭示知识图谱表示模型的内在原理和影响因素,探究其在特定任务上的效果差异。【方法】 面向链接预测任务,采用对比研究方法,比较基于翻译的知识图谱表示模型和基于语义匹配的知识图谱表示模型在FB15K、WN18、FB15K-237和WN18RR这4个数据集上的效果差异。【结果】 在Hits@1指标上,TuckER模型在WN18、FB15K-237、WN18RR数据集上取得最优值（分别为0.946 0、0.263 3和0.443 0）;ComplEx模型在FB15K数据集上取得了最优值（0.731 4）。【局限】 由于篇幅限制,实证研究仅比较了知识图谱表示模型在链接预测任务和知识库问答上的效果,尚未比较在信息检索、推荐系统等任务上的差异。【结论】 基于翻译的知识图谱表示模型和基于语义匹配的知识图谱表示模型存在显著差异,知识图谱表示模型的得分函数、负采样和优化方法等模型结构,以及训练数据比例等因素对其在链接预测任务上的效果存在显著影响。

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	余传明
	张贞港
	孔令格

关键词 ：知识图谱, 表示学习, 深度学习, 链接预测

Abstract：

[Objective] This study systematically reviews the internal mechanism and influencing factors of knowledge graph representation models, aiming to investigate their impacts on specific tasks. [Methods] For the link prediction task, we compared the performance of translation-based and semantic matching-based knowledge graph representation models on FB15K, WN18, FB15K-237 and WN18RR datasets. [Results] With the Hits@1 indicator, the TuckER model generated the best value on WN18, FB15K-237 and WN18RR datasets (0.946 0, 0.263 3 and 0.443 0, respectively), while the ComplEx model yielded the highest value on FB15K dataset (0.731 4). [Limitations] We only compared the effects of knowledge graph representation model on the link prediction and knowledge base QA tasks. More research is needed to examine their performance on information retrieval, recommendation system and other tasks. [Conclusions] There are significant differences between the translation-based and the semantic matching-based knowledge graph representation models. The score function, negative sampling, and optimization method of the knowledge graph representation model, as well as the proportion of training data have significant impacts on the results of the link prediction.

Key words： Knowledge Graph Representation Learning Deep Learning Link Prediction

收稿日期: 2021-05-18 出版日期: 2021-12-23

ZTFLH:

TP391

基金资助:*国家自然科学基金重大课题(71790612);国家自然科学基金面上项目(71974202)

通讯作者: 余传明,ORCID：0000-0001-7099-0853 E-mail: yucm@zuel.edu.cn

引用本文:

余传明, 张贞港, 孔令格. 面向链接预测的知识图谱表示模型对比研究^*[J]. 数据分析与知识发现, 2021, 5(11): 29-44.
Yu Chuanming, Zhang Zhengang, Kong Lingge. Comparing Knowledge Graph Representation Models for Link Prediction. Data Analysis and Knowledge Discovery, 2021, 5(11): 29-44.

链接本文:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.0491 或 https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I11/29

Fig.1 基于知识图谱表示模型的链接预测框架

模型	得分函数	参数复杂度
TransE	$\| \| h + r - t \| \| P$	$O (n e k + n r k)$
TransH	$\| \| (h - r T hr) + r - (t - r T tr) \| \| P$	$O (n e k + 2 n r k)$
TransR	$\| \| hM r + r - t M r \| \| P$	$O (n e k + n r k + n r k 2)$
TransD	$\| \| h M rh + r - t M rt \| \| P$	$O (2 n e k + 2 n r k)$
DisMult	$< h, r, t >$	$O (n e k + n r k)$
ComplEx	$< h, r, t >$	$O (2 n e k + 2 n r k)$
ConvE	$f (vec (f ([h, r] × w) W) t$	$O (n e k + n r k)$
HypER	$f (vec (h × ve c - 1 (w r M)) W) t$	$O (n e k + n r k)$
TuckER	$< h, r, t, T >$	$O (n e k + n r k + k 3)$

Table 1 不同模型的得分函数和参数复杂度

Table 2 数据集详细信息

Table 3 模型参数设置

Table 4 知识图谱表示模型获取表示在FB15K和WN18数据集上的效果

Table 5 知识图谱表示模型获取表示在FB15K-237和WN18RR数据集上的效果

Fig.2 维度对基于翻译的知识图谱表示模型效果的影响

Fig.3 维度对基于语义匹配的知识图谱表示模型效果的影响

Fig.4 负采样方法和数量对TransE模型效果的影响

Fig.5 负采样方法和数量对TransH模型效果的影响

Fig.6 负采样方法和数量对TransR模型效果的影响

Fig.7 负采样方法和数量对TransD模型效果的影响

Fig.8 随机失活对知识图谱表示模型效果的影响

Fig.9 标签平滑对知识图谱表示模型效果的影响

Fig.10 动态新增实体对知识图谱表示模型效果的影响

Fig.11 训练数据比例对于基于翻译的知识图谱表示模型的效果影响

Fig.12 训练数据比例对于基于语义匹配的知识图谱表示模型的效果影响

Table 6 知识图谱表示模型在知识库问答任务的效果对比

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