2026-09-21T07:58:14Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/16875 2023-07-10 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Chang, Kevin C-C. Pham, Cuong K. 2010-08-20T18:00:32Z 2010-08-20T18:00:32Z 2010-08-20T18:00:32Z 2010-08 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2010-07-16T19:03:54Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 3 Pham_KimCuong.pdf: 560512 bytes, checksum: fc9825c9a5562e70b1c3a29975b2ac67 (MD5) msthesis-objectsearch-kimpham-jul10.pdf: 560512 bytes, checksum: fc9825c9a5562e70b1c3a29975b2ac67 (MD5) Pham_KimCuong.pdf: 567002 bytes, checksum: 7161e8f3649a226cff168671046bc4e3 (MD5) Made available in DSpace on 2010-08-20T18:00:32Z (GMT). No. of bitstreams: 4 Pham_KimCuong.pdf: 560512 bytes, checksum: fc9825c9a5562e70b1c3a29975b2ac67 (MD5) msthesis-objectsearch-kimpham-jul10.pdf: 560512 bytes, checksum: fc9825c9a5562e70b1c3a29975b2ac67 (MD5) 1_Pham_KimCuong.pdf: 567002 bytes, checksum: 7161e8f3649a226cff168671046bc4e3 (MD5) license.txt: 4060 bytes, checksum: d15f4bb274332f93701bcbc67b940bf5 (MD5) http://hdl.handle.net/2142/16875 en Copyright 2010 by Cuong Kim Pham. All rights reserved. semantics web search engines learning to rank information retrieval structured query object search Object search: supporting structured queries in web search engines Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. MS:Computer Science -UIUC 10KS0112MS