融合关系和结构编码的规则抽取与推理研究

doi:10.3969/j.issn.1000-5641.2025.01.008

摘要/Abstract

摘要：

领域知识图谱拥有不完备性和语义复杂多样性的特点, 从而导致其在规则抽取和选择问题上的不足, 影响了其推理的能力. 针对此问题, 提出了一种融合关系和结构编码的规则抽取模型. 通过提取目标子图中的关系和结构信息并进行特征编码, 从而实现了一种多维度的嵌入表达方法. 设计了融合关系和结构信息的自注意力机制, 使模型能够更好地捕捉输入序列中的依赖关系和局部结构信息, 从而提升了模型对于上下文的理解和表达能力, 进而解决了在语义复杂情况下规则的抽取和选择的问题. 通过在真实汽车部件故障工业数据集和公共数据集的实验, 表明了在链接预测与规则质量评估任务中, 所提出的模型都有一定的提升 (规则长度为3时, mean reciprocal rank (MRR) 平均提升了7.1百分点, Hits@10平均提升了8.6百分点; 规则长度为2时, MRR平均提升了7.4百分点, Hits@10平均提升了3.9百分点), 证实了关系和结构信息对于规则抽取与推理的有效性.

关键词: 工业知识图谱, 规则抽取, 规则推理, 知识补全, 链接预测

Abstract:

The domain knowledge graph exhibits characteristics of incompleteness and semantic complexity, which lead to shortcomings in the extraction and selection of rules, thereby affecting its inferential capabilities. A rule extraction model that integrates relationship and structural encoding is proposed to address this issue. A multidimensional embedding approach is achieved by extracting relational and structural information from the target subgraph and conducting feature encoding. A self-attention mechanism is designed to integrate relational and structural information, enabling the model to capture dependency relationships and local structural information in the input sequence better. This enhancement improves the understanding and expressive capabilities of context of the model, thus addressing the challenges of rule extraction and selection in the complex semantic situations. The experimental results for actual industrial datasets of automotive component failures and public datasets demonstrate improvements in the proposed model for link prediction and rule quality evaluation tasks. When the rule length is 3, an average increase of 7.1 percentage points in the mean reciprocal rank (MRR) and an average increase of 8.6 percentage points in Hits@10 are observed. For a rule length of 2, an average increase of 7.4 percentage points in MRR and an average increase of 3.9 percentage points in Hits@10 are observed. This confirms the effectiveness of relational and structural information in rule extraction and inference.

Key words: industrial knowledge graph, rule extraction, rule reasoning, knowledge completion, link prediction

中图分类号:

TP391

胡继米, 万卫兵, 程锋, 赵宇明. 融合关系和结构编码的规则抽取与推理研究[J]. 华东师范大学学报（自然科学版）, 2025, 2025(1): 97-110.

Jimi HU, Weibing WAN, Feng CHENG, Yuming ZHAO. Rule extraction and reasoning for fusing relation and structure encoding[J]. J* E* C* N* U* N* S*, 2025, 2025(1): 97-110.

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数据集	Triplet/个	Entity/个	Relation/个
FB15K-237^[19]	310116	14541	237
WN18RR^[20]	93003	40943	11
UMLS^[18]	5960	135	46
car-repair	148175	42419	10

环境(参数)	版本(值)
Python	3.7
PyTorch	1.11
GPU	RTX 3090
Optimizer	Adam
num_head	6
num_layers	4
lr	0.0003
d_v	64
Dropout	0.1

规则长度	模型	MRR			Hits@10/%
规则长度	模型	FB15K-237	WN18RR	UMLS	FB15K-237	WN18RR	UMLS
	TransE^[8]	0.294	0.226	0.668	46.5	50.1	93.0
	R-GCN^[11]	0.249	–	–	41.7	–	–
2	Neural LP^[21]	0.189	0.371	0.751	31.3	39.6	94.0
	Ruleformer^[15]	0.237	0.381	0.851	36.0	41.1	98.8
	DRUM^[14]	0.225	0.379	0.791	35.8	40.9	96.8
	SRRule	0.277	0.385	0.862	36.8	41.5	98.2
3	Neural LP^[21]	0.239	0.425	0.735	39.9	49.2	92.3
	Ruleformer^[15]	0.342	0.452	0.857	51.3	53.0	98.4
	DRUM^[14]	0.328	0.441	0.784	49.9	51.6	97.2
	SRRule	0.385	0.458	0.865	52.4	53.8	98.9

规则长度	模型	MRR	Hits@10/%
2	Neural LP^[21]	0.211	32.1
	Ruleformer^[15]	0.287	34.4
	DRUM^[14]	0.214	32.4
	SRRule	0.312	36.8
3	Neural LP^[21]	0.253	35.4
	Ruleformer^[15]	0.334	48.7
	DRUM^[14]	0.304	45.2
	SRRule	0.368	51.7

模型	MRR		Hits@10/%
模型	car-repair	FB15K-237	car-repair	FB15K-237
SRRule-rel	0.342	0.352	48.9	49.4
SRRule-sruc	0.314	0.334	45.7	46.2
SRRule-none	0.292	0.241	39.4	36.7
SRRule	0.368	0.385	51.7	52.4