Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10555–10573 | Cite as

Automatic extraction and visualization of semantic relations between medical entities from medicine instructions

Article

Abstract

Recent years have witnessed the rapid development and tremendous research interests in healthcare domain. The health and medical knowledge can be acquired from many sources, such as professional health providers, health community generated data and textual descriptions of medicines. This paper explores the classification and extraction of semantic relation between medical entities from the unstructured medicine Chinese instructions. In this paper, three kinds of textual features are extracted from medicine instruction according to the nature of natural language texts. And then, a support vector machine based classification model is proposed to categorize the semantic relations between medical entities into the corresponding semantic relation types. Finally, the extraction algorithm is utilized to obtain the semantic relation triples. This paper also visualizes the semantic relations between medical entities with relationship graph for their future processing. The experimental results show that the approach proposed in this paper is effective and efficient in the classification and extraction of semantic relations between medical entities.

Keywords

Semantic relation Medical entity Classification model Extraction algorithm Semantic relation triple Semantic relationship graph 

Notes

Acknowledgments

The work presented in this paper is partially supported by the National Natural Science Foundation of China under Grant No. 61100133 and the Major Projects of National Social Science Foundation of China under Grant No. 11&ZD189.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial SystemWuhanChina

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