Abstract
Currently there appear only few practical semantic web applications. The reason is mainly in that a large number of existed web documents contain only machine-unreadable information on which software agent can do nothing. There have been some works devoting to web document annotation manually or semi-automatically to solve this problem. This paper presents an automatic approach for web document annotation based on specific domain ontology. Because complete semantic annotation of web document is still a tough task, we simplify the problem by annotating ontology concept instances on web documents and propose an Ontology Instance Learning (OIL) method to extract instances from structure and free text of web documents. These instances of the ontology concept will be used to annotate web pages in the related domain. Our OIL method exhibits quite good performance in real life web documents as shown in our experiment.
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Shu, W., Enhong, C. (2004). An Instance Learning Approach for Automatic Semantic Annotation. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_148
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DOI: https://doi.org/10.1007/978-3-540-30497-5_148
Publisher Name: Springer, Berlin, Heidelberg
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