Object search: supporting structured queries in web search engines
type: CreativeWork
description: As the web evolves, increasing quantities of structured information is embedded in web pages in disparate formats. For example, a digital camera’s description may include its price and megapixels whereas a professor’s description may include her name, university, and research interests. Both types of pages may include 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 structured queries 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 avoid explicit IE. We empirically show that with minimum additional human effort, our system outperforms a GSE with respect to structured queries with clear object semantics.
abstract: As the web evolves, increasing quantities of structured information is embedded in web pages in disparate formats. For example, a digital camera’s description may include its price and megapixels whereas a professor’s description may include her name, university, and research interests. Both types of pages may include 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 structured queries 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 avoid explicit IE. We empirically show that with minimum additional human effort, our system outperforms a GSE with respect to structured queries with clear object semantics.
author: Pham, Cuong K.
language: en
keywords: semantics, web search engines, learning to rank, information retrieval, structured query, object search
date published: 2010-08-20T18:00:32Z
date modified: 2026-07-22T22:25:09Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/16875