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Automatic Semantic Annotation Using Machine Learning

Automatic Semantic Annotation Using Machine Learning

Jie Tang, Duo Zhang, Limin Yao, Yi Li
Copyright: © 2009 |Pages: 45
ISBN13: 9781605660288|ISBN10: 1605660280|ISBN13 Softcover: 9781616925208|EISBN13: 9781605660295
DOI: 10.4018/978-1-60566-028-8.ch006
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MLA

Tang, Jie, et al. "Automatic Semantic Annotation Using Machine Learning." The Semantic Web for Knowledge and Data Management, edited by Zongmin Ma and Huaiqing Wang, IGI Global, 2009, pp. 106-150. https://doi.org/10.4018/978-1-60566-028-8.ch006

APA

Tang, J., Zhang, D., Yao, L., & Li, Y. (2009). Automatic Semantic Annotation Using Machine Learning. In Z. Ma & H. Wang (Eds.), The Semantic Web for Knowledge and Data Management (pp. 106-150). IGI Global. https://doi.org/10.4018/978-1-60566-028-8.ch006

Chicago

Tang, Jie, et al. "Automatic Semantic Annotation Using Machine Learning." In The Semantic Web for Knowledge and Data Management, edited by Zongmin Ma and Huaiqing Wang, 106-150. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-028-8.ch006

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Abstract

This chapter aims to give a thorough investigation of the techniques for automatic semantic annotation. The Semantic Web provides a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries. However, lack of annotated semantic data is a bottleneck to make the Semantic Web vision a reality. Therefore, it is indeed necessary to automate the process of semantic annotation. In the past few years, there was a rapid expansion of activities in the semantic annotation area. Many methods have been proposed for automating the annotation process. However, due to the heterogeneity and the lack of structure of the Web data, automated discovery of the targeted or unexpected knowledge information still present many challenging research problems. In this chapter, we study the problems of semantic annotation and introduce the state-of-the-art methods for dealing with the problems. We will also give a brief survey of the developed systems based on the methods. Several real-world applications of semantic annotation will be introduced as well. Finally, some emerging challenges in semantic annotation will be discussed.

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