Constructing biomedical domain-specific knowledge graph with minimum supervision

被引:0
作者
Jianbo Yuan
Zhiwei Jin
Han Guo
Hongxia Jin
Xianchao Zhang
Tristram Smith
Jiebo Luo
机构
[1] University of Rochester,Department of Computer Science
[2] Chinese Academy of Sciences,Institute of Computing Technology
[3] Samsung Research America,School of Software Technology
[4] Dalian University of Technology,Department of Pediatrics
[5] University of Rochester Medical Center,undefined
来源
Knowledge and Information Systems | 2020年 / 62卷
关键词
Knowledge graph construction; Biomedical; Domain-specific; Minimum supervision;
D O I
暂无
中图分类号
学科分类号
摘要
Domain-specific knowledge graph is an effective way to represent complex domain knowledge in a structured format and has shown great success in real-world applications. Most existing work on knowledge graph construction and completion shares several limitations in that sufficient external resources such as large-scale knowledge graphs and concept ontologies are required as the starting point. However, such extensive domain-specific labeling is highly time-consuming and requires special expertise, especially in biomedical domains. Therefore, knowledge extraction from unstructured contexts with minimum supervision is crucial in biomedical fields. In this paper, we propose a versatile approach for knowledge graph construction with minimum supervision based on unstructured biomedical domain-specific contexts including the steps of entity recognition, unsupervised entity and relation embedding, latent relation generation via clustering, relation refinement and relation assignment to assign cluster-level labels. The experimental results based on 24,687 unstructured biomedical science abstracts show that the proposed framework can effectively extract 16,192 structured facts with high precision. Moreover, we demonstrate that the constructed knowledge graph is a sufficient resource for the task of knowledge graph completion and new knowledge inference from unseen contexts.
引用
收藏
页码:317 / 336
页数:19
相关论文
共 54 条
[1]  
Ashburner M(2000)Gene ontology: tool for the unification of biology Nat Genet 25 25-716
[2]  
Ball CA(2016)A method for exploring implicit concept relatedness in biomedical knowledge network BMC Bioinform 17 265-D169
[3]  
Blake JA(2008)Bio2RDF: towards a mashup to build bioinformatics knowledge systems J Biomed Inform 41 706-998
[4]  
Botstein D(2016)Uniprot: the universal protein knowledgebase Nucleic Acids Res 45 D158-195
[5]  
Butler H(2015)Knowlife: a versatile approach for constructing a large knowledge graph for biomedical sciences BMC Bioinform 16 157-33
[6]  
Cherry JM(2015)Mining strong relevance between heterogeneous entities from unstructured biomedical data Data Min Knowl Discov 29 976-28
[7]  
Davis AP(2015)Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia Semant Web 6 167-477
[8]  
Dolinski K(2016)A review of relational machine learning for knowledge graphs Proc IEEE 104 11-65
[9]  
Dwight SS(2012)Deepdive: web-scale knowledge-base construction using statistical learning and inference VLDS 12 25-undefined
[10]  
Eppig JT(2003)The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text J Biomed Inform 36 462-undefined