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R OAD R UNNER : Towards Automatic Data Extraction from Large Web Sites

R OAD R UNNER : Towards Automatic Data Extraction from Large Web Sites. Valter Crescenzi Giansalvatore Mecca Paolo Merialdo. VLDB 2001. Overview. Automatically generates a wrapper from large structured Web pages Supports nested structures

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R OAD R UNNER : Towards Automatic Data Extraction from Large Web Sites

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  1. ROADRUNNER: Towards Automatic Data Extraction from Large Web Sites Valter Crescenzi Giansalvatore Mecca Paolo Merialdo VLDB 2001

  2. Overview • Automatically generates a wrapper from large structured Web pages • Supports nested structures • Efficient approach to large, complex pages with regular structures

  3. Approach • Given a set of example pages • Generate a Union-free Regular Expression (UFRE) • Find the least upper bounds on the RE lattice to generate a wrapper • Reduces to find the least upper bound on two UFRES

  4. Matching/Mismatching • Start with the first page and create a RE that defines the wrapper • Match each successive sample against the wrapper • Mismatches result in generalizations of the regular expression • Types of mismatches • String mismatches • Tag mismatches

  5. Example Pages

  6. Example #PCDATA • String mismatches are used to discover fields of the documents • Wrapper is generated by replacing “John Smith” with #PCDATA

  7. Example (Cont.) #PCDATA Tag Mismatches :Discovering Optionals • First check to see if mismatch is caused by an iterator • If not, could be an optional field in wrapper or sample • Cross search used to determine possible optionals • Image field determined to be optional • (<img src=…/>)?

  8. Example (Cont.) #PCDATA (<IMG src=…/>)? Tag Mismatches :Discovering Optionals • First check to see if mismatch is caused by an iterator • If not, could be an optional field in wrapper or sample • Cross search used to determine possible optionals • Image field determined to be optional • (<img src=…/>)?

  9. Tag Mismatches :Discovering Iterators • Assume mismatch is caused by repeated elements in a list • Match possible squares against earlier squares • Generalize the wrapper by finding all contiguous repeated occurrences • (<li><i>Title:</i>#PCDATA</li>)+ Example (Cont.) #PCDATA (<IMG src=…/>)? #PCDATA #PCDATA

  10. Extracted Result

  11. Recursive Example

  12. Complexity

  13. Discussion • Assumptions • Pages are well-structured • Want to extract at the level of entire fields • Structure can be modeled without disjunctions • Search Space for explaining mismatches is huge • Uses a number of heuristics to prune space • Limited backtracking • Limit on number of choices to explore • Patterns can not be delimited by optionals • Will result in pruning possible wrappers

  14. Experimental Result

  15. Comparison with Other Works

  16. X means the information extraction system has the capability; X* means the information extraction system has the ability as long as the training corpus can accommodate the required training data; ? Shows that the systems can has the ability in somewhat degree; * means that the extraction pattern itself doesn’t show the ability, but the overall system has the capability.

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