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数据分析与知识发现  2020, Vol. 4 Issue (11): 1-14     https://doi.org/10.11925/infotech.2096-3467.2020.0681
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于领域知识图谱的生命医学学科知识发现探析*
胡正银1,2(),刘蕾蕾1,2,代冰1,2,覃筱楚3,4
1中国科学院成都文献情报中心 成都 610041
2中国科学院大学经济与管理学院图书情报与档案管理系 北京 100190
3广州市再生医学与健康广东省实验室 广州 510700
4中国科学院广州生物医药与健康研究院 广州 510530
Discovering Subject Knowledge in Life and Medical Sciences with Knowledge Graph
Hu Zhengyin1,2(),Liu Leilei1,2,Dai Bing1,2,Qin Xiaochu3,4
1Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
3Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou 510700, China
4Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
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摘要 

【目的】 探讨融合多源数据,开展深层次学科知识发现研究与服务的方法。【方法】 通过构建科技文献SPO语义网络形成领域知识图谱的核心;通过“实体对齐、概念层次融合与关系融合”实现多源异构数据融合,生成完整领域知识图谱;基于领域知识图谱开展深层次学科知识发现;选择造血干细胞癌症治疗进行实证研究。【结果】 提出一套基于知识图谱的学科知识发现方法框架KGSKD,可多维度、细粒度融合多源异构数据,定义数据间复杂语义关系,原生支持知识推理、路径发现、链路预测等知识发现应用。【局限】 KGSKD存在容易出现数据过饱和、知识发现过程可解释性较差、与领域专家沟通难度较高等局限。【结论】 KGSKD具有数据类型更丰富、知识关联更全面、挖掘方法更先进、发现结果更深入等优势,可更有效地支持生命医学学科深层次知识发现研究与服务。

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胡正银
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代冰
覃筱楚
关键词 学科知识发现知识图谱SPO三元组数据融合实体对齐    
Abstract

[Objective] This paper explores new methods for deep subject knowledge discovery using multi-source heterogeneous data. [Methods] First, we constructed a SPO semantic network of literature to create the core domain knowledge graph. Then, we implemented multi-source heterogeneous data fusion through “entity alignment, concept level fusion and relationship fusion” to obtain the whole domain knowledge graph. Finally, we discovered deep subject knowledge with the help of this knowledge graph. We examined our method with data on Hematopoietic Stem Cell for Cancer Treatment (HSCCT). [Results] This paper proposed a knowledge graph-based framework for subject knowledge discovery (KGSKD), which fuses multi-source heterogeneous data multi-dimensionally and fine-grainedly, enriches semantic relationships among data, and supports knowledge discovery techniques such as knowledge inference, pathfinder, and link prediction natively. [Limitations] KGSKD has some limitations including data supersaturation, poor interpretability of knowledge discovery results and difficulty in communicating with domain experts. [Conclusions] KGSKD has the advantages of “richer data types”, “more comprehensive knowledge linkage”, “more advanced mining methods” and “deeper discovery results”, which effectively supports research and services of deep knowledge discovery in life sciences and medicine.

Key wordsSubject Knowledge Discovery    Knowledge Graph    SPO Triples    Data Fusion    Entity Alignment
收稿日期: 2020-07-13      出版日期: 2020-12-04
ZTFLH:  G251  
基金资助:*本文系中国科学院“十三五”信息化专项“面向干细胞领域知识发现的科研信息化应用”(XXH13506-203);中国科学院文献情报能力建设专项“科技知识服务大数据基础设施”(Y9290002);科技部创新方法工作专项“基于群智理论的创新方法新系统研究与应用示范”的研究成果之一(2019IM020100)
通讯作者: 胡正银     E-mail: huzy@clas.ac.cn
引用本文:   
胡正银,刘蕾蕾,代冰,覃筱楚. 基于领域知识图谱的生命医学学科知识发现探析*[J]. 数据分析与知识发现, 2020, 4(11): 1-14.
Hu Zhengyin,Liu Leilei,Dai Bing,Qin Xiaochu. Discovering Subject Knowledge in Life and Medical Sciences with Knowledge Graph. Data Analysis and Knowledge Discovery, 2020, 4(11): 1-14.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0681      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I11/1
Fig.1  封闭式知识发现与开放式知识发现示意图[17]
序号 主语 主语语义类型 谓语 宾语 宾语语义类型
1 Hemofiltration Therapeutic or Preventive Procedure TREATS Patients Human
2 Digoxin overdose Injury or Poisoning PROCESS_OF Patients Human
3 Hyperkalemia Pathologic Function COMPLICATES Digoxin overdose Injury or Poisoning
4 Hemofiltration Therapeutic or Preventive Procedure TREATS (INFER) Digoxin overdose Injury or Poisoning
Table 1  SPO三元组示例
Fig.2  KGSKD方法框架
Fig.3  SPO语义网络示例图[22]
序号 映射类型 源知识实体(Term) 目标知识实体(CUI|Concept Name|STY)*
1 一对一映射 Abnormality of neutrophils C0427515| Neutrophil abnormality| Finding
2 多对一映射 Central Nervous System Neoplasms C0085136| Central Nervous System Neoplasms| Neoplastic Process
CNS TUMORS
CNS NEOPL
3 一对多映射 RUNX1 C1335654|RUNX1 gene| Gene or Genome
C1435548| RUNX1 protein, human| Amino Acid, Peptide, or Protein
4 一对无映射 Conjunctival icterus ——
Table 2  知识实体向UMLS映射[30,31]
Fig.4  基于知识图谱的知识发现方法
类型 数据库 检索策略 数据量
论文 PubMed (((((((stem cells) OR stem cell)) AND (((((stem cellulose) OR stem. Cellular) OR cello) OR cellar) OR cellphone))) OR ((((((((((((ESC) OR ASC) OR iPS) OR PGC) OR MSC) OR CSC) OR LSC) OR TSC) OR ADSC) OR HSC)) near ((cell) OR cells)))) AND ((Hematopoiet*) AND stem cell*)
出版日期:2009/01/01-2017/12/31
24 051篇
专利 Derwent
Innovation
((((ALLD=(("stem cells" OR "stem cell") NOT ("stem cellulose" or "stem. Cellular" or "cello" or "cellar" or "cellphone")) OR ALLD=((ESC or ASC or iPS or PGC or MSC or CSC or LSC or TSC or ADSC or HSC) near (cells OR cell)) OR ALLD=(("totipotent" or "pluripotent" or "multipotent" or "unipotent" or "progenitor" or "precursor") ADJ (cells OR cell)) OR ALLD=("tissue engineer*" OR "tissue scaffolding " OR "tissue regenerat*of regenerative medicine" OR "tissue expansion of regenerative medicine" OR "tissue therapy of regenerative medicine" OR "tissue culture of regenerative medicine" OR "tissue construction of regenerative medicine" OR "biological material*" OR "animal seed cells") OR ABD=(("skin" OR "cartilage" OR "bone" OR "tendon" OR "myocardiac" OR "cardiac" OR "vascular" OR "nerve" OR "cornea" OR "dental" OR "periodontal") ADJ ("tissue engineer*" or "regenerat*")) OR ALLD=("tissue engineer*" AND biomaterial*) OR SSTO=("regenerative medicine") OR ICR=("C12N0050735" OR "C12N005074" OR "C12N0050789" OR "C12N0050797" OR "C12N005095")) NOT ALLD=("seed*" or "herbicide insect hybrid" or "hybrid" or "root bud seeding" or "hybrid corn " or "plant tissue seed") NOT ALLD=(("fuel cell" or "in-plane switching" or "Intrusion Prevention System") NOT (("non-pluripotent") ADJ (CELL*))) NOT ICR=(H or D or E or F or A01B or A01C or A01H or A01G or A21 or A22 or A23 or A46 or A24 or A47 or A63 or A62 or A44 or A45 or C02 or C03C or C05or OR C06 or C10 or C21 or C07B or C07C or C07D or C07F or C07J))) AND (CC=((WO OR US OR EP OR JP)))) AND (ALLD=(Hematopoiet* and stem cell*));
申请年:1999年-2018年
3 986件
Table 3  HSC科技文献检索策略与检索结果
语义类型(英文) 语义类型(中文)
Chemicals_Drug 化学物质与药物
Disorder 疾病
Genes_Molecular_Sequence 基因与分子序列
Phenotype 表型
Mutation 突变
Hallmark 癌症标识物
Phenomena 现象
Procedure 程序活动
Device 设备
Physiology 生理学
Concepts(including gene, cell, virus, etc.) 概念(包含基因、细胞、病毒等)
Living_Being 生物
PMID 论文
PN 专利
Table 4  HSCCT知识实体语义类型[30]
语义关系对象 语义分组
(Semantic Group)
语义关系(Semantic Relationship)
知识实体
-
知识实体
相互作用关系 ASSOCIATED_WITH(mutation_to_disease, mutation_to_phenotype, gene_to_mutation, gene_to_disease, gene_to_phenotype, gene Related);
INTERACT_WITH;PRODUCE;CONVERT_TO;COEXIST_WITH
功能关系 TREAT;PREVENT;USE;METHOD_OF;ADMINISTERED_TO
表象关系 DIAGNOSE;MANIFESTATION_OF
比较关系 HIGH_THAN;LOWER_THAN;
SAME_AS;ISA
位置关系 PART_OF;LOCATION_OF;OCCUR_IN
影响关系 AFFECT;PROMOTE;DISRUPT;CAUSE;INHITBIT; PREDISPOSE;AUGMENT;PRECEDE;PROCESS_OF;
COMPLICATE;STIMULATE;
共现关系 cooccurrence
知识实体
-
科技文献
隶属关系 belong_to_PMID
belong_to_PN
Table 5  HSCCT语义关系[30]
Fig.5  疫苗和胎盘生长因子关联路径
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