Object search: supporting structured queries in web search engines

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2010

Author dc:creator

• Pham, Cuong K.

Contributors dc:contributor

• Chang, Kevin C-C.

• semantics
• web search engines
• learning to rank
• information retrieval
• structured query
• object search

Statement dc:rights

• Copyright 2010 by Cuong Kim Pham. All rights reserved.

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/16875

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/16875