动态聚合实体和关系上下文的知识图谱补全

doi:10.19678/j.issn.1000-3428.0065410

摘要/Abstract

摘要：

目前关于知识图谱补全的研究通常只进行实体嵌入增强，弱化了关系附加信息对链路预测的影响，并忽略了知识图谱中实体和关系的结构关联关系。为解决上述问题，提出一种动态聚合实体和关系上下文的知识图谱补全模型(DAERC)。采用编码器-解码器结构，获取并筛选最优上下文来增强实体和关系表示。编码器使用改进的图注意力机制设计关系和实体聚合器，以累乘的方式动态聚合上下文信息，并将关系聚合器的输出应用于实体聚合过程，缓解了因独立聚合造成的实体关系交互性较弱的问题。解码器通过不同的评分函数获取每个候选三元组的得分，验证三元组的合理性。实验结果表明，DAERC有效地增强了TransE、ConvE、RotatE这3类知识图谱嵌入模型的实体和关系嵌入表示能力，在FB15k-237和WN18RR数据集上Hits@10指标相较于表现最好的对照CoNE模型分别提升约5.2%、2.1%、1.7%和7.2%、2.3%、2.2%。

关键词: 知识图谱补全, 图结构, 上下文, 注意力系数, 动态聚合

Abstract:

Most of the recent research on Knowledge Graph Completion(KGC) usually enhances entity embedding alone, which weakens the impact of additional information about relation on link prediction and ignores the structural association relation between entities and those in the knowledge graph.To address these issues, a KGC model with dynamic aggregation of the contexts of entities and relations, called DAERC, is proposed. The model is an encoder-decoder architecture; it obtains and filters the optimal contexts to enhance entity and relation embedding representation. The encoder uses an improved graph attention mechanism to design a relation and an entity aggregator that dynamically aggregates context information by multiplying. Furthermore, it applies the output of the relation aggregator to the entity aggregator, and alleviates the problem of weak entity-relation interactivity caused by independent aggregation.The decoder obtains the score of each candidate triplet through different score functions to predict the plausibility for each triplet. The results reveal that the DAERC effectively enhances the ability to embed and represent entities and relations for three different Knowledge Graph Embedding(KGE) model: TransE, ConvE, and RotatE. On the FB15k-237 dataset, compared to the best performing CoNE model applied to TransE, ConvE, and RotatE, DAERC's Hits@10 is increased by approximately 5.2%, 2.1%, and 1.7%, and improved by approximately 7.2%, 2.3%, and 2.2% on the WN18RR dataset.

Key words: Knowledge Graph Completion(KGC), graph structure, context, attention coefficient, dynamic aggregation

马坤, 安敬民, 李冠宇. 动态聚合实体和关系上下文的知识图谱补全[J]. 计算机工程, 2023, 49(8): 77-84, 95.

Kun MA, Jingmin AN, Guanyu LI. Knowledge Graph Completion with Dynamically Aggregating Context of Entity and Relation[J]. Computer Engineering, 2023, 49(8): 77-84, 95.

https://www.ecice06.com/CN/Y2023/V49/I8/77

图/表 10

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模型	评分函数	损失函数
TransE	$ {F}_{\mathrm{T}}=\mathrm{ }-‖{\boldsymbol{e}}_{\mathrm{h}}+\boldsymbol{r}-{\boldsymbol{e}}_{\mathrm{t}}‖ $	$ {L}_{\mathrm{T}}=\sum\limits_{t\in T}\sum\limits_{{t}^{\text{'}}\in {T}^{\text{'}}}\mathrm{m}\mathrm{a}\mathrm{x}(0, \gamma -F(t)+F({t}^{\text{'}}\left)\right) $
ConvE	$ {F}_{\mathrm{C}}=f\left(\mathrm{v}\mathrm{e}\mathrm{c}\right(f\left(\right[{\stackrel{-}{\boldsymbol{e}}}_{\mathrm{h}};\stackrel{-}{\boldsymbol{r}}\left]\mathrm{*}\omega \right)\left)\boldsymbol{W}\right){\boldsymbol{e}}_{\mathrm{t}} $	$ {L}_{\mathrm{C}}=\mathrm{ }-\frac{1}{N}\sum\limits_{t\in T}\ln F\left(t\right)+\sum\limits_{{t}^{\text{'}}\in {T}^{\text{'}}}\ln(1-F({t}^{\text{'}}\left)\right) $
RotatE	$ {F}_{\mathrm{R}}=-‖{\boldsymbol{e}}_{\mathrm{h}, \mathrm{c}}\circ {\boldsymbol{r}}_{\mathrm{c}}-{\boldsymbol{e}}_{\mathrm{t}, \mathrm{c}}‖ $	$ {L}_{\mathrm{R}}=\mathrm{ }-\sum\limits_{t\in T}\ln\sigma (\gamma -F(t\left)\right)-\frac{1}{{N}_{{T}^{\text{'}}}}\sum\limits_{{t}^{\text{'}}\in {T}^{\text{'}}}\ln\sigma \left(F\right({t}^{\text{'}})-\gamma \left.\right) $

模型	评分函数	损失函数
TransE	$ {F}_{\mathrm{T}}=\mathrm{ }-‖{\boldsymbol{e}}_{\mathrm{h}}+\boldsymbol{r}-{\boldsymbol{e}}_{\mathrm{t}}‖ $	$ {L}_{\mathrm{T}}=\sum\limits_{t\in T}\sum\limits_{{t}^{\text{'}}\in {T}^{\text{'}}}\mathrm{m}\mathrm{a}\mathrm{x}(0, \gamma -F(t)+F({t}^{\text{'}}\left)\right) $
ConvE	$ {F}_{\mathrm{C}}=f\left(\mathrm{v}\mathrm{e}\mathrm{c}\right(f\left(\right[{\stackrel{-}{\boldsymbol{e}}}_{\mathrm{h}};\stackrel{-}{\boldsymbol{r}}\left]\mathrm{*}\omega \right)\left)\boldsymbol{W}\right){\boldsymbol{e}}_{\mathrm{t}} $	$ {L}_{\mathrm{C}}=\mathrm{ }-\frac{1}{N}\sum\limits_{t\in T}\ln F\left(t\right)+\sum\limits_{{t}^{\text{'}}\in {T}^{\text{'}}}\ln(1-F({t}^{\text{'}}\left)\right) $
RotatE	$ {F}_{\mathrm{R}}=-‖{\boldsymbol{e}}_{\mathrm{h}, \mathrm{c}}\circ {\boldsymbol{r}}_{\mathrm{c}}-{\boldsymbol{e}}_{\mathrm{t}, \mathrm{c}}‖ $	$ {L}_{\mathrm{R}}=\mathrm{ }-\sum\limits_{t\in T}\ln\sigma (\gamma -F(t\left)\right)-\frac{1}{{N}_{{T}^{\text{'}}}}\sum\limits_{{t}^{\text{'}}\in {T}^{\text{'}}}\ln\sigma \left(F\right({t}^{\text{'}})-\gamma \left.\right) $

数据集	#Relation	#Entity	#Train	#Valid	#Test
FB15k-237	237	14 541	272 115	17 535	20 466
WN18RR	11	40 943	86 835	3 034	3 134

数据集	#Relation	#Entity	#Train	#Valid	#Test
FB15k-237	237	14 541	272 115	17 535	20 466
WN18RR	11	40 943	86 835	3 034	3 134

模型	FB15k-237				WN18RR
模型	MR	MRR	Hits@10/%	Hits@1/%	MR	MRR	Hits@10/%	Hits@1/%
TransE	221	24.2	42.4	16.6	379 3	18.0	45.7	1.3
DistMult	309	22.4	38.3	14.6	511 0	43.0	49.0	39.0
ComplEx	289	22.7	39.1	14.5	526 1	44.0	51.0	41.0
ANALOGY	309	22.7	38.7	14.8	898 2	39.2	41.0	38.4
ConvE	244	31.6	50.1	23.7	418 7	43.0	52.0	40.0
RotatE	177	33.8	53.3	24.1	334 0	47.6	57.1	42.8
CoNE-TransE	211	30.6	48.5	21.7	354 2	22.7	50.2	6.4
CoNE-ConvE	187	33.4	51.5	24.6	416 5	45.5	52.7	42.3
CoNE-RotatE	147	35.0	53.6	25.8	277 6	49.4	58.2	45.1
DAERC-TransE	167	32.2	50.9	22.9	232 4	28.1	53.8	11.3
DAERC-ConvE	179	33.8	52.6	24.9	415 4	46.0	53.9	43.0
DAERC-RotatE	162	35.4	54.5	26.2	275 2	50.5	59.5	46.1

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