As the web evolves, increasing quantities of structured information is embeddedin web pages in disparate formats. For example, a digital camera???s description may include its price and megapixels whereas a professor???s description may includeher name, university, and research interests. Both types of pages mayinclude additional ambiguous information. General search engines (GSEs) do not support queries over these types of data because they ignore the web document semantics. Conversely, describing requisite semantics through structuredqueries into databases populated by information extraction (IE) techniques are expensive and not easily adaptable to new domains. This paper describes a methodology for rapidly developing search engines capable of answering structured queries over unstructured corpora by utilizing machine learning to avoidexplicit IE. We empirically show that with minimum additional human effort, our system outperforms a GSE with respect to structured queries with clear object semantics.
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