时间感知组合的动态知识图谱补全

doi:10.3785/j.issn.1008-973X.2024.08.020

浙江大学学报(工学版)

2024, Vol. 58

Issue (8): 1738-1747 DOI: 10.3785/j.issn.1008-973X.2024.08.020

计算机技术、控制工程

时间感知组合的动态知识图谱补全

李忠良1,2(

),陈麒1,2,石琳1,2,*(

),杨朝1,2,邹先明1,2

1. 华北理工大学人工智能学院，河北唐山 063210
2. 河北省工业智能感知重点实验室，河北唐山 063210

Dynamic knowledge graph completion of temporal aware combination

Zhongliang LI1,2(

),Qi CHEN1,2,Lin SHI1,2,*(

),Chao YANG1,2,Xianming ZOU1,2

1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
2. Hebei Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, China

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摘要：

针对现有时序知识图谱嵌入方法仅考虑时序信息的关系或仅编码独立的时序向量，知识图谱补全性能不高的问题，提出时间感知组合（TAC）的时序知识图谱补全方法. 通过建模维度特征，分析时序信息对知识图谱补全方法的有效程度. 通过时序信息内嵌和独立相结合的嵌入方式，考虑时序信息嵌入后，不同学习方式对表示学习能力产生不同的影响. 提出的方法利用长短时记忆（LSTM）网络编码时序信息，学习到更准确的时间维度特征，有助于提升时序图谱的性能. 在ICEWS14、ICEWS05-15和GDELT数据集上进行实验，验证了时间感知组合方法的有效性. 对比相关的研究性能指标可知，本文方法在链接预测上表现较优.

关键词： 时序知识图谱; 注意力机制; 长短时记忆(LSTM); 时序嵌入

Abstract:

A time-aware combination (TAC) method for temporal knowledge graph completion was proposed aiming at the problem that the existing temporal knowledge graph embedding methods only consider the relationship of temporal information or encode independent temporal vectors and the completion performance of these methods is not high enough. The effectiveness of temporal information on knowledge graph completion methods was analyzed by modeling dimensional features. Different learning methods have different effects on the representation learning ability after considering the embedding of temporal information through the embedding method of combining the embedded and independent temporal information. Long short-term memory (LSTM) network was utilized to encode temporal information, learn more accurate temporal dimension features and help to improve the performance of temporal graph. Experiments on ICEWS14, ICEWS05-15 and GDELT datasets verified the effectiveness of the time-aware combination method. The related research performance metrics were compared. Results show that the proposed method performs better in link prediction.

Key words: temporal knowledge graph attention mechanism long short-term memory (LSTM) temporal embedding

收稿日期: 2023-07-10 出版日期: 2024-07-23

CLC:

TP 391

通讯作者: 石琳 E-mail: bbzqlzl@163.com;shilin@ncst.edu.cn

作者简介: 李忠良（1994—），男，硕士生，从事知识图谱嵌入的研究. orcid.org/0009-0001-2675-1043. E-mail：bbzqlzl@163.com

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引用本文:

李忠良,陈麒,石琳,杨朝,邹先明. 时间感知组合的动态知识图谱补全[J]. 浙江大学学报(工学版), 2024, 58(8): 1738-1747.

Zhongliang LI,Qi CHEN,Lin SHI,Chao YANG,Xianming ZOU. Dynamic knowledge graph completion of temporal aware combination. Journal of ZheJiang University (Engineering Science), 2024, 58(8): 1738-1747.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.08.020 或 https://www.zjujournals.com/eng/CN/Y2024/V58/I8/1738

补全方法	适用知识库	建模维度	时间特征	时间维度融合	相似性评价函数
TransE	三元组	s,r,o	×	×	$ f\left( {s,r,o} \right) = \left\\| {{{\boldsymbol{e}}_s}+{{\boldsymbol{e}}_r} - {{\boldsymbol{e}}_o}} \right\\| $
DistMult	三元组	s,r,o	×	×	$ f\left( {s,r,o} \right) = \left\langle {{{\boldsymbol{e}}_s},{{\boldsymbol{e}}_r},{{\boldsymbol{e}}_o}} \right\rangle $
ComplEx	三元组	s,r,o	×	×	$ f\left( {s,r,o} \right) = {{\mathrm{Re}}} \left( {\langle {{\boldsymbol{e}}_s},{{\boldsymbol{w}}_r},{{\boldsymbol{e}}_o}\rangle } \right) $
ConvE	三元组	s,r,o	×	×	$ f\left( {s,r,o} \right) = f({\mathrm{vec}}(f([{{\boldsymbol{\bar e}}_s};{{\boldsymbol{\bar r}}_r}] * {\boldsymbol{\varOmega}})){{\boldsymbol{W}}}){{\boldsymbol{e}}_o} $
ConvKB	三元组	s,r,o	×	×	$ f\left( {s,r,o} \right) = {\text{concat}}(f([{{\boldsymbol{e}}_s},{{\boldsymbol{e}}_r},{{\boldsymbol{e}}_o}] * {\boldsymbol{\varOmega}} )) \cdot {\boldsymbol{w}} $
HyTE	四元组	s,r,o	√	√	$ f\left( {s,r,o,t} \right) = \left\\| {{P_t}({{\boldsymbol{e}}_s})+{P_t}({{\boldsymbol{e}}_r}) - {P_t}({{\boldsymbol{e}}_o})} \right\\| $
TA-TransE	四元组	s,r,o	√	√	$ f\left( {s,r,o,t} \right) = \left\\| {{{\boldsymbol{e}}_s}+{{\boldsymbol{e}}_{{r_{{\mathrm{seq}}}}}} - {{\boldsymbol{e}}_o}} \right\\| $
TA-DistMult	四元组	s,r,o	√	√	$ f\left( {s,r,o,t} \right) = ({{\boldsymbol{e}}_s} \circ {{\boldsymbol{e}}_o}){{\boldsymbol{e}}_{{r_{{\mathrm{seq}}}}}}^{\text{T}} $
ST-ConvKB	四元组	s,r,o	√	√	$ f\left( {s,r,o,t} \right) = {\text{concat}}(f([{{\boldsymbol{e}}_{{s_t}}},{{\boldsymbol{e}}_r},{{\boldsymbol{e}}_{{o_t}}}] * {\boldsymbol{\varOmega}} )) \cdot {\boldsymbol{w}} $
TTransE	四元组	s,r,o,t	√	×	$ f\left( {s,r,o,t} \right) = \left\\| {{{\boldsymbol{e}}_s}+{{\boldsymbol{e}}_r}+{{\boldsymbol{e}}_t} - {{\boldsymbol{e}}_o}} \right\\| $
TComplEx	四元组	s,r,o,t	√	×	$ f\left( {s,r,o,t} \right) = {{\mathrm{Re}}} (\langle {{\boldsymbol{e}}_s},{{\boldsymbol{w}}_r},{{\boldsymbol{e}}_o},{{\boldsymbol{w}}_t}\rangle ) $
RE-GCN^[27]	四元组	s,r,o	√	√	$ \vec p(o\|s,r,{{\boldsymbol{H}}_t},{R_t}) = \sigma ({{\boldsymbol{H}}_t}{\text{ConvTransE}}({{\boldsymbol{e}}_{{s_t}}},{{\boldsymbol{e}}_{{r_t}}})) $
ATiSE	四元组	s,r,o	√	√	$ f\left( {s,r,o,t} \right) = {D_{{\mathrm{KL}}}}({{\boldsymbol{P}}_{r,t}},{{\boldsymbol{P}}_{e,t}}) $
TeRo	四元组	s,r,o	√	√	$ f\left( {s,r,o,t} \right) = \|\|{{\boldsymbol{e}}_{{s_t}}}+{{\boldsymbol{e}}_r} - \overline {{{\boldsymbol{e}}_{ot}}} \|\| $

表 1 用于知识图谱的补全方法对比

表 2 时间序列标记

图 1 时间感知组合的嵌入方式

表 3 数据集的信息统计

表 4 不同嵌入方法在ICEWS14、ICEWS05-15和GDELT的链接预测结果

表 5 不同嵌入方法在ICEWS14和ICEWS11-14数据集上的实验结果对比

表 6 不同时序嵌入方法在ICEWS14数据集上的实验结果

图 2 嵌入维数的消融对比

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