Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules

doi:10.3390/app131910660

	Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
	Lan, Yinyu1,2 ; He, Shizhu1,2 ; Liu, Kang1,2 ; Zhao, Jun1,2
发表期刊	APPLIED SCIENCES-BASEL
	2023-10-01
卷号	13 期号:19 页码:17
通讯作者	He, Shizhu(shizhu.he@nlpr.ia.ac.cn)
摘要	Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: (1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. (2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations of two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7% and 4.3% in mean reciprocal rank (MRR).
关键词	distributed representation knowledge graph link prediction logical rule
DOI	10.3390/app131910660
收录类别	SCI
语种	英语
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:001086871600001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54311
专题	复杂系统认知与决策实验室多模态人工智能系统全国重点实验室_自然语言处理
通讯作者	He, Shizhu
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Lan, Yinyu,He, Shizhu,Liu, Kang,et al. Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules[J]. APPLIED SCIENCES-BASEL,2023,13(19):17.
APA	Lan, Yinyu,He, Shizhu,Liu, Kang,&Zhao, Jun.(2023).Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules.APPLIED SCIENCES-BASEL,13(19),17.
MLA	Lan, Yinyu,et al."Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules".APPLIED SCIENCES-BASEL 13.19(2023):17.