Authors: D. M. Campbell, D. W. Embley, D.W. Lonsdale, R.D. Smith, S. W. Liddle, Y. -K. Ng, Y. S. Jiang
Tags: 1999, conceptual modeling
Electronically available data on the Web is exploding at an ever increasing pace. Much of this data is unstructured,which makes searching hard and traditional database querying impossible. Many Web documents, however, contain anabundance of recognizable constants that together describe the essence of a document’s content. For these kinds ofdata-rich, multiple-record documents (e.g., advertisements, movie reviews, weather reports, travel information, sportssummaries, Ænancial statements, obituaries, and many others) we can apply a conceptual-modeling approach to extractand structure data automatically. The approach is based on an ontology ± a conceptual model instance ± that describesthe data of interest, including relationships, lexical appearance, and context keywords. By parsing the ontology, we canautomatically produce a database scheme and recognizers for constants and keywords, and then invoke routines torecognize and extract data from unstructured documents and structure it according to the generated database scheme.Experiments show that it is possible to achieve good recall and precision ratios for documents that are rich in rec-ognizable constants and narrow in ontological breadth. Our approach is less labor-intensive than other approaches thatmanually or semiautomatically generate wrappers, and it is generally insensitive to changes in Web-page for-mat.Read the full paper here: https://reader.elsevier.com/reader/sd/pii/S0169023X99000270?token=3E89BD5E913083633E23D65265F47FB69A022521C0436F019DA278144FE29B2AAB46F19C506767F78042CCAB704E6872
