Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion

doi:10.1186/s12911-021-01622-7

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 自然语言处理

	Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion
	Lan,Yinyu1,2 ; He,Shizhu1,2 ; Liu,Kang1,2 ; Zeng,Xiangrong 3; Liu,Shengping 3; Zhao,Jun1,2
发表期刊	BMC Medical Informatics and Decision Making
	2021-11-29
卷号	21 期号:Suppl 9
通讯作者	He,Shizhu(shizhu.he@nlpr.ia.ac.cn)
摘要	AbstractBackgroundKnowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness.MethodsTo address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation.ResultsExperiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations.ConclusionsIn this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.
关键词	Medical knowledge graph completion Path-based knowledge reasoning Textual semantic representation Pre-trained language model
DOI	10.1186/s12911-021-01622-7
语种	英语
WOS记录号	BMC:10.1186/s12911-021-01622-7
出版者	BioMed Central
七大方向——子方向分类	知识表示与推理
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46127
专题	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 Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation 2.University of Chinese Academy of Sciences; School of Artificial Intelligence 3.Beijing Unisound Information Technology Co., Ltd
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Lan,Yinyu,He,Shizhu,Liu,Kang,et al. Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion[J]. BMC Medical Informatics and Decision Making,2021,21(Suppl 9).
APA	Lan,Yinyu,He,Shizhu,Liu,Kang,Zeng,Xiangrong,Liu,Shengping,&Zhao,Jun.(2021).Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion.BMC Medical Informatics and Decision Making,21(Suppl 9).
MLA	Lan,Yinyu,et al."Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion".BMC Medical Informatics and Decision Making 21.Suppl 9(2021).