Automatic ontology-based knowledge extraction from web documents. Alani, H., Kim, S., Millard, D., E., Weal, M., J., Hall, W., Lewis, P., H., & Shadbolt, N., R. IEEE Intelligent Systems, 18(1):14-21, IEEE Computer Society, 2003.
Automatic ontology-based knowledge extraction from web documents [pdf]Paper  Automatic ontology-based knowledge extraction from web documents [link]Website  abstract   bibtex   
To bring the Semantic Web to life and provide advanced knowledge services, we need efficient ways to access and extract knowledge from Web documents. Although Web page annotations could facilitate such knowledge gathering, annotations are rare and will probably never be rich or detailed enough to cover all the knowledge these documents contain. Manual annotation is impractical and unscalable, and automatic annotation tools remain largely undeveloped. Specialized knowledge services therefore require tools that can search and extract specific knowledge directly from unstructured text on the Web, guided by an ontology that details what type of knowledge to harvest. An ontology uses concepts and relations to classify domain knowledge. Other researchers have used ontologies to support knowledge extraction,1,2 but few have explored their full potential in this domain. The Artequakt project links a knowledge-extraction tool with an ontology to achieve continuous knowledge support and guide information extraction. The extraction tool searches online documents and extracts knowledge that matches the given classification structure. It provides this knowledge in a machine-readable format that will be automatically maintained in a knowledge base (KB). Users could further enhance knowledge extraction using a lexicon-based term expansion mechanism that provides extended ontology terminology.

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