Automatically Extracting Ontologically Specified Data from HTML Tables of Unknown Structure
Data on the Web in HTML tables is mostly structured, but we usually do not know the structure in advance. Thus, we cannot directly query for data of interest. We propose a solution to this problem based on document-independent extraction ontologies. The solution entails elements of table understanding, data integration, and wrapper creation. Table understanding allows us to recognize attributes and values, pair attributes with values, and form records. Data-integration techniques allow us to match source records with a target schema. Ontologically specified wrappers allow us to extract data from source records into a target schema. Experimental results show that we can successfully map data of interest from source HTML tables with unknown structure to a given target database schema. We can thus “directly” query source data with unknown structure through a known target schema.
KeywordsTarget Attribute Target Schema Merge Attribute Source Schema Source Table
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- [Aut01]autoscanada.com, Summer 2001.Google Scholar
- [BE02]J. Biskup and D.W. Embley. Extracting information from heterogeneous information sources using ontologically specified target views. Information Systems, 2002. (to appear).Google Scholar
- [Bob02]http://www.bobhowardhonda.com, January 2002.
- [DDH01]A. Doan, P. Domingos, and A. Halevy. Reconciling schemas of disparate data sources: A machine-learning approach. In Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data (SIG-MOD 2001), pages 509–520, Santa Barbara, California, May 2001.Google Scholar
- [DEG]Homepage for BYU data extraction research group. URL: http://osm7.cs.byu.edu/deg/index.html.
- [ECJ+99]D.W. Embley, D.M. Campbell, Y. S. Jiang, S.W. Liddle, D.W. Lonsdale, Y.-K. Ng, and R.D. Smith. Conceptual-model-based data extraction from multiple-record Web pages. Data & Knowledge Engineering, 31(3):227–251, November 1999.Google Scholar
- [KS91]H. F. Korth and A. Silberschatz. Database System Concepts. McGraw-Hill, Inc., New York, New York, second edition, 1991.Google Scholar
- [LN99a]S. Lim and Y. Ng. An automated approach for retrieving heirarchical data from HTML tables. In Proceedings of the Eighth International Conference on Informaiton and Knowledge management (CIKM’99), pages 466–474, Kansas City, Missouri, November 1999.Google Scholar
- [LN99b]D. Lopresti and G. Nagy. Automated table processing: An (opinionated) survey. In Proceedings of the Third IAPR Workshop on Graphics Recognition, pages 109–134, Jaipur, India, September 1999.Google Scholar
- [LYE01]S.W. Liddle, S.H. Yau, and D.W. Embley. On the automatic extraction of data from the hidden web. In Proceedings of the International Workshop on Data Semantics in Web Information Systems (DASWIS-2001), pages 106–119, Yokohama, Japan, November 2001.Google Scholar
- [MBR01]J. Madhavan, P. A. Bernstein, and E. Rahm. Generic schema matching with Cupid. In Proceedings of the 27th International Conference on Very Large Data Bases (VLDB’01), pages 49–58, Rome, Italy, September 2001.Google Scholar
- [MHH00]R. Miller, L. Haas, and M. A. Hernandez. Schema mapping as query discovery. In Proceedings of the 26th International Conference on Very Large Databases (VLDB’00), pages 77–88, Cairo, Egypt, September 2000.Google Scholar
- [RGM01]S. Raghavan and H. Garcia-Molina. Crawling the hidden web. In Proceedings of the 27th International Conference on Very Large Data Bases (VLDB’01), Rome, Italy, September 2001.Google Scholar