Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services.
With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establi...
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 13 |
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| Autores principales: | , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/12/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121235673&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121235673 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/12/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 121235673 121235673 121235673 10.1155/2017/2858423 121235673 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services. aug: au: Shi, Longxiang Li, Shijian Yang, Xiaoran Qi, Jiaheng Pan, Gang Zhou, Binbin affil: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China sug: subj: Health Knowledge Evaluation Semantics Graphics Human Access to Information Evaluation Health Information Diffusion of Innovation Conceptual Framework Information Systems Data Analysis Program Implementation Funding Source ab: With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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