HackerPapers

arXiv:1103.1252cs.AI

Automatic Wrapper Adaptation by Tree Edit Distance Matching

Emilio Ferrara, Robert Baumgartner

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

Information distributed through the Web keeps growing faster day by day, and for this reason, several techniques for extracting Web data have been suggested during last years. Often, extraction tasks are performed through so called wrappers, procedures extracting information from Web pages, e.g. implementing logic-based techniques. Many fields of application today require a strong degree of robustness of wrappers, in order not to compromise assets of information or reliability of data extracted. Unfortunately, wrappers may fail in the task of extracting data from a Web page, if its structure changes, sometimes even slightly, thus requiring the exploiting of new techniques to be automatically held so as to adapt the wrapper to the new structure of the page, in case of failure. In this work we present a novel approach of automatic wrapper adaptation based on the measurement of similarity of trees through improved tree edit distance matching techniques.

arXiv comments
7 pages, 3 figures, In Proceedings of the 2nd International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2010)
Journal reference
Combinations of Intelligent Methods and Applications Smart Innovation, Systems and Technologies Volume 8, 2011, pp 41-54
DOI
doi:10.1007/978-3-642-19618-8_3
Subjects
cs.AIArtificial Intelligence cs.IRInformation Retrieval

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Mar 2011 to Mar 2011. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.