Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion
文献类型：期刊论文
| 作者 | Lan,Yinyu1,2 ; He,Shizhu1,2; Liu,Kang1,2; Zeng,Xiangrong3; Liu,Shengping3; Zhao,Jun1,2
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| 刊名 | BMC Medical Informatics and Decision Making
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| 出版日期 | 2021-11-29 |
| 卷号 | 21期号:Suppl 9 |
| 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 |
| 通讯作者 | He,Shizhu(shizhu.he@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/S12911-021-01622-7 |
| 出版者 | BioMed Central |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46126]  |
| 专题 | 模式识别国家重点实验室_自然语言处理 |
| 通讯作者 | He,Shizhu |
| 作者单位 | 1.University of Chinese Academy of Sciences; School of Artificial Intelligence 2.Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation 3.Beijing Unisound Information Technology Co., Ltd |
| 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). |
入库方式： OAI收割
来源：自动化研究所
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