Abstract
This paper addresses the issue of semantic annotation using horizontal and vertical contexts. Semantic annotation is a task of annotating web pages with ontological information. As information on a web page is usually two-dimensionally laid out, previous semantic annotation methods that view a web page as an ‘object’ sequence have limitations. In this paper, to better incorporate the two-dimensional contexts, semantic annotation is formalized as a problem of block detection and text annotation. Block detection is aimed at detecting the text block by making use of context in one dimension and text annotation is aimed at detecting the ‘targeted instance’ in the identified blocks using the other dimensional context. A two-stage method for semantic annotation using machine learning has been proposed. Experimental results indicate that the proposed method can significantly outperform the baseline method as well as the sequence-based method for semantic annotation.
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References
Benjamins, R., Contreras, J.: Six Challenges for the Semantic Web. Intelligent Software Components. In: Intelligent software for the networked economy (isoco) (April 2002)
Kushmerick, N., Weld, D.S., Doorenbos, R.B.: Wrapper Induction for Information Extraction. In: Proc. of IJCAI, Nagoya, Japan, pp. 729–737 (1997)
Ciravegna, F. (LP)2, an Adaptive Algorithm for Information Extraction from Web-related Texts. In: Proc. of the IJCAI 2001 Workshop on Adaptive Text Extraction and Mining, Seattle, USA (August 2001)
Handschuh, S., Staab, S., Ciravegna, F.: S-CREAM – semi-automatic cREAtion of metadata. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 358–372. Springer, Heidelberg (2002)
Tang, J., Li, J., Lu, H., Liang, B., Wang, K.: iASA: Learning to Annotate the Semantic Web. Journal on Data Semantic 4, 110–145 (2005)
Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20, 273–297 (1995)
Reeve, L.: Integrating Hidden Markov Models into Semantic Web Annotation Platforms. Technique Report (2004)
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Hong, M., Tang, J., Li, J. (2006). Semantic Annotation Using Horizontal and Vertical Contexts. In: Mizoguchi, R., Shi, Z., Giunchiglia, F. (eds) The Semantic Web – ASWC 2006. ASWC 2006. Lecture Notes in Computer Science, vol 4185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11836025_6
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DOI: https://doi.org/10.1007/11836025_6
Publisher Name: Springer, Berlin, Heidelberg
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