Abstract
The relationship of biomedical entity is the cornerstone of acquiring biomedical knowledge. It is of great significance to the construction of related databases in the biomedical field and the management of medical literature. How to quickly and accurately extract the required relationships of biomedical entity from massive unstructured literature is an important research. In order to improve accuracy, we use support vector machine (SVM) which is a machine learning algorithm based on feature vectors to extract relationships of entities. We extract the five main relationships in medical literature, including ISA, PART_OF, CAUSES, TREATS and DIAGNOSES. First of all, related topics are used to search medical literature from PubMed database, such as disease-drug, cause-disease. These documents are used as experimental data and then processed to form a corpus. In selection of features, the method of information gain is used to select the influential entities’ own features and entities’ context features. On this basis, semantic predicates are added as a feature to improve accuracy. The experimental results show that the accuracy of extraction is increased by 5%–10%. In the end, Resource Description Framework (RDF) is used to store extracted relationships from the corresponding documents, and it provides support for the subsequent retrieval of related documents.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, Z.: Research of Text Mining Technology in Biomedical Field. Dalian University of Technology, Dalian (2008)
Li, F., Liu, S., Liu, Z.: A review of semantic relation extraction methods in biomedicine. Libr. Forum 6, 61–69 (2017)
Hassan, M., Makkaoui, O., Coulet, A., et al.: Extracting disease-symptom relationships by learning syntactic patterns from dependency graphs. In: Proceedings of the 2015 Workshop on Biomedical Natural Language Processing (BioNLP 2015), pp. 71–80 (2015)
Zheng, W., Lin, H., Zhao, Z., et al.: A graph kernel based on context vectors for extracting drug–drug interactions. J Biomed Inform. 61, 34–43 (2016)
Bi, H., et al.: The extraction of Chinese entity’s relation based on semantic and SVM. In: National Conference on Information Storage Technology (2012)
Kilicoglu, H., Fiszman, M., Rodriguez, A., Shin, D.: AM ripple. Semantic MEDLINE: a web application for managing the results of PubMed searches. In: Proceedings of Smbm, pp. 69–76 (2008)
Fang, L.: Research on Two Stage Named Entity Recognition of Chinese Micro-Blog Based on CRF. Xihua University, Chengdu (2015)
Xiu Yan, W., et al.: Extracting semantic relations between biomedical entities by hybrid method. Mod. Library Inf. Technol. 29(3), 77–82 (2013)
Cristianini, N., Shawe-Taylor, J., Li, G., Wang, M., Zeng, H.J.: Introduction of Support Vector Machine. Publishing House of Electronics Industry, Beijing (2004)
Hang, Li: Statistical Machine Learning. Tsinghua University Press, Beijing (2012)
Zhang, Y., Xu, J., Chen, H., et al.: Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning. J. Biol. Databases Curation (2016)
He, H.: Research of Word Representations on Biomedical Named Entity Recognition. Dalian University of Technology, Dalian (2015)
Collobert, R., Weston, J., Bottou, L., et al.: Natural language processing (almost) fromscratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
LIBSVM: A library for support vector machines [CP/DK]. https://www.csie.ntu.edu.tw
Gao, X.: The Construction of Entity Relationship Model Based on RDF(S) Resource Query. Jilin University, Changchun (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, X., Lin, S., Huang, Z. (2018). Extraction of Semantic Relations from Medical Literature Based on Semantic Predicates and SVM. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-01078-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01077-5
Online ISBN: 978-3-030-01078-2
eBook Packages: Computer ScienceComputer Science (R0)