ABSTRACT
We present an original approach to the automatic induction of wrappers for sources of the hidden Web that does not need any human supervision. Our approach only needs domain knowledge expressed as a set of concept names and concept instances. There are two parts in extracting valuable data from hidden-Web sources: understanding the structure of a given HTML form and relating its fields to concepts of the domain, and understanding how resulting records are represented in an HTML result page. For the former problem, we use a combination of heuristics and of probing with domain instances; for the latter, we use a supervised machine learning technique adapted to tree-like information on an automatic, imperfect, and imprecise, annotation using the domain knowledge. We show experiments that demonstrate the validity and potential of the approach.
- L. Barbosa and J. Freire. Siphoning hidden-Web data through keyword-based interfaces. In Proc. Simpósio Brasileiro de Bancos de Dados, Brasília, Brasil, Oct. 2004.Google Scholar
- BrightPlanet. The deep Web: Surfacing hidden value. White Paper, July 2001.Google Scholar
- J. Carme, R. Gilleron, A. Lemay, and J. Niehren. Interactive learning of node selecting tree transducers. Machine Learning Journal, 66(1):33--67, Jan. 2007. Google ScholarDigital Library
- J. Caverlee, L. Liu, and D. Buttler. Probe, cluster, and discover: Focused extraction of qa-pagelets from the deep Web. In Proc. ICDE, Boston, USA, Mar. 2004. Google ScholarDigital Library
- S.-L. Chuang, K. C.-C. Chang, and C. Zhai. Context-aware wrapping: Synchronized data extraction. In Proc. VLDB, Vienna, Austria, Sept. 2007. Google ScholarDigital Library
- V. Crescenzi, G. Mecca, and P. Merialdo. Roadrunner: Towards automatic data extraction from large Web sites. In Proc. VLDB, Roma, Italy, Sept. 2001. Google ScholarDigital Library
- R. B. Doorenbos, O. Etzioni, and D. S. Weld. A scalable comparison-shopping agent for the World-Wide Web. In Proc. Agents, Marina del Ray, USA, Feb. 1997. Google ScholarDigital Library
- D. Freitag and N. Kushmerick. Boosted wrapper induction. In Proc. AAAI, Austin, USA, July 2000. Google ScholarDigital Library
- J. Geng and J. Yang. AUTOBIB: Automatic extraction of bibliographic information on the web. In Proc. IDEAS, 2004. Google ScholarDigital Library
- B. He, M. Patel, Z. Zhang, and K. C.-C. Chang. Accessing the deep Web: A survey. Communications of the ACM, 50(2):94--101, May 2007. Google ScholarDigital Library
- P. G. Ipeirotis and L. Gravano. Distributed search over the hidden Web: Hierarchical database sampling and selection. In Proc. VLDB, Hong Kong, China, Aug. 2002. Google ScholarDigital Library
- F. Jousse, R. Gilleron, I. Tellier, and M. Tommasi. Conditional Random Fields for XML trees. In Proc. ECML Workshop on Mining and Learning in Graphs, Berlin, Germany, Sept. 2006.Google Scholar
- J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data. In Proc. ICML, Williamstown, USA, June 2001. Google ScholarDigital Library
- I. Mansuri and S. Sarawagi. A system for integrating unstructured data into relational databases. In Proc. ICDE, 2006. Google ScholarDigital Library
- A. McCallum and W. Li. Early results for named entity recognition with conditional random fields. In Proc. CoNLL, Edmonton, Canada, May 2003. Google ScholarDigital Library
- A. Mittal. Probing the hidden Web. Research internship report. Technical Report 479, Gemo, INRIA Futurs, July 2007.Google Scholar
- D. Muschick. Unsupervised learning of XML tree annotations. Master's thesis, Université de Technologie de Lille and Technischen Universität Graz, June 2007.Google Scholar
- I. Muslea, S. Minton, and C. A. Knoblock. Hierarchical wrapper induction for semistructured information sources. Proc. AAMAS, 4(1-2):93--114, 2001. Google ScholarDigital Library
- D. Pinto, A. McCallum, X. Wei, and W. B. Croft. Table extraction using conditional random fields. In Proc. SIGIR, Toronto, Canada, July 2003. Google ScholarDigital Library
- M. F. Porter. An algorithm for suffix stripping. Program, 14(3):130--137, July 1980.Google ScholarCross Ref
- Princeton University Cognitive Science Laboratory. WordNet. http://wordnet.princeton.edu/.Google Scholar
- S. Raghavan and H. Garcia-Molina. Crawling the hidden Web. In Proc. VLDB, Roma, Italy, Sept. 2001. Google ScholarDigital Library
- S. Sarawagi and W. W. Cohen. Semi-Markov conditional random fields for information extraction. In Proc. NIPS, Vancouver, Canada, Dec. 2004.Google Scholar
- P. Senellart. Comprendre le Web caché. Understanding the Hidden Web. PhD thesis, Université Paris-Sud 11, Orsay, France, Dec. 2007.Google Scholar
- F. Sha and F. Pereira. Shallow parsing with conditional random fields. In Proc. HLT-NAACL, Edmonton, Canada, May 2003. Google ScholarDigital Library
- A. Smith and M. Osborne. Using gazetteers in discriminative information extraction. In Proc. CoNLL, New York, USA, June 2006. Google ScholarDigital Library
- B. Thomas. Bottom-up learning of logic programs for information extraction from hypertext documents. In Proc. PKDD, Catvat-Dubrovnik, Croatia, Sept. 2003.Google ScholarCross Ref
- W3C. HTML 4.01 specification, Sept. 1999. http://www.w3.org/TR/REC-html40/.Google Scholar
- W3C. Web Services Description Language (WSDL) 1.1, Mar. 2001. http://www.w3.org/TR/wsdl.Google Scholar
- W. Wu, A. Doan, C. T. Yu, and W. Meng. Bootstrapping domain ontology for semantic Web services from source Web sites. In Proc. Technologies for E-Services, Trondheim, Norway, Sept. 2005. Google ScholarDigital Library
- Y. Zhai and B. Liu. Web data extraction based on partial tree alignment. In Proc. WWW, Chiba, Japan, May 2005. Google ScholarDigital Library
- Z. Zhang, B. He, and K. C.-C. Chang. Understanding Web query interfaces: best-effort parsing with hidden syntax. In Proc. SIGMOD, Paris, France, June 2004. Google ScholarDigital Library
- Z. Zhang, B. He, and K. C.-C. Chang. Light-weight domain-based form assistant: Querying Web databases on the fly. In Proc. VLDB, Trondheim, Norway, Sept. 2005. Google ScholarDigital Library
Index Terms
- Automatic wrapper induction from hidden-web sources with domain knowledge
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