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BioMed Research International
Volume 2017 (2017), Article ID 2858423, 12 pages
https://doi.org/10.1155/2017/2858423
Research Article

Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services

College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Correspondence should be addressed to Shijian Li; nc.ude.ujz@ilnaijihs

Received 16 August 2016; Revised 28 November 2016; Accepted 22 December 2016; Published 12 February 2017

Academic Editor: Uwe Kreimeier

Copyright © 2017 Longxiang Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.