Generating a Hematopoietic Stem Cell Knowledge Graph for Scientific Knowledge Discovery

doi:10.11871/jfdc.10-1649.2021.06.006

Abstract

Abstract:

[Objective] The hematopoietic stem cell (HSC) is one kind of the most effective stem cells for clinical treatments. It is of great significance to discover important knowledge entities, knowledge relations, and knowledge paths by literature mining for HSC knowledge discovery. Knowledge graph (KG), which represents knowledge entities and their relations with more details in a simple manner is widely used in scientific knowledge discovery (SKD).[Methods] This paper proposes a framework of generating KG using Subject-Predicate-Object (SPO) triples from literature, which includes six processes: literature retrieval, SPO extracting, SPO cleanup, SPO ranking, discovery pattern integrating, and graph building. Then, an HSC KG was constructed based on the Neo4j graph database following the framework. Finally, three kinds of SKD scenarios using HSC KG are introduced by empirical analysis. [Results] The results show that HSC KG has the advantages of “using graph data structure”, “integrating discovery patterns”, “fusing native graph mining algorithms”, and “easy to use”, which can effectively support deep open discovery, close discovery, and topic discovery in HSC.

Key words: knowledge graph, SPO triple, scientific knowledge discovery, literature mining, hematopoietic stem cell

HU Zhengyin,LIU Leilei,CHEN Wenjie,LIU Chunjiang,QIAN Li,SONG Yibing. Generating a Hematopoietic Stem Cell Knowledge Graph for Scientific Knowledge Discovery[J]. Frontiers of Data and Computing, 2021, 3(6): 81-97.

Figures/Tables 10

References 29

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Literature type	Database	Retrieval strategy	Number
Paper	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) Publication Date：2010/01/01 - 2019/12/31	21,098
Patent	DI	(((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”) NOT (“non-pluripotent”)) ADJ (cells OR cell))) NOT ALLD=(“fuel cell” or “in-plane switching” or “Intrusion Prevention System”) NOT IC=(H or D or E or F or A23 or A46 or A24 or A47 or A63 or A62 or A44 or A45 or C07B or C07C or C07D or C07F or C07J)) AND ICR=(C12N) AND SSTO=(“HEMATOPOIETIC STEM CELL” “HSC”)):AY>=(2010) AND AY<=(2019): CC=(WO OR US OR EP OR JP):1 AND 2 AND 3 Application Year：2010 - 2019	4,786

A-B relations	B-C relations	A-C relations
<INHIBITS, STIMULATES,COEXISTS_WITH, INTERACTS_WITH>	<ASSOCIATED_WITH>	May_Treat
< INHIBITS >	<CAUSES>	May_Disrupt
<INHIBITS, COEXISTS_WITH, INTERACTS_WITH>	<PREVENTS>	May_Prevent
<AFFECTS >	<AFFECTS, DISRUPTS >	Indirect_Affect

Label	Semantic Type Abbreviation	Number	Samples
DISEASE	dsyn, emod, sosy	1,485	Atherosclerosis, Myocardial Infarction, Diabetes, Acute coronary syndrome, Hematological Disease…
GENE	genf, gngm	3,106	CD34 gene, CCR9 gene, KDR gene, CXCR4 Receptors, Interleukin 7 Receptor, Interleukin-6…
CELL	celc, celf, cell, comd	890	HSC, Stem cells, Bone Marrow Cells, Endothelial Cells, Jurkat Cells, cytokine secretion, ES Cell Line…
TISSUE	ortf, tisu	226	Umbilical Cord Blood, Endothelium, Transplanted tissue, Bone Matrix, Myocardium…
VIRUS	virs	101	HIV, Herpesvirus 6, Retroviridae, Adenoviruses, BK Virus, Cytomegalovirus…
Others (Organism, Receptor, Vitamin, …)	other semantic types	8,809	Granulocyte Colony-Stimulating Factor, Hepatocyte Growth Factor, MicroRNAs…

	Semantic Group	Relationships Type
Knowledge Entity — Knowledge Entity	Interaction Group	ASSOCIATED_WITH, INTERACT_WITH, PRODUCE, CONVERTS_TO, COEXISTS_WITH
	Function Group	TREAT, PREVENT, USE, MEASURE, METHOD_OF, ADMINISTERED_TO
	Distinguish Group	DIAGNOSES, MANIFESTATION_OF
	Compare Group	HIGH_THAN, LOWER_THAN, compared_with
	Location Group	OCCURS_IN
	Affect Group	AFFECTS, DISRUPTS, CAUSES, INHITBITS, PREDISPOSE, AUGMENTS, PRECEDE, PROCESS_OF, COMPLICATES, STIMULATE,
Knowledge Entity— Literature	Subordination	belong_to
SPO— Literature	Subordination	belong_to

Name	CUI	Frequency
Bone remodeling	C0085268	22
Tissue homeostasis	C1523351	20
Natural regeneration	C0034963	17
Bone metabolism	C0596204	15
Cytokine production	C1327413	15
Degenerative abnormality	C0011164	14
Dysmyelopoietic Syndromes	C0026986	11
Heart valve disease	C0018824	10