Gathering meta-data and instances from object referral lists on the web

Srinivas Vadrevu, Fatih Gelgi, Saravanakumar Nagarajan, Hasan Davulcu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Purpose - The purpose of this research is to automatically separate and extract meta-data and instance information from various link pages in the web, by utilizing presentation and linkage regularities on the web. Design/methodology/approach - Research objectives have been achieved through an information extraction system called semantic partitioner that automatically organizes the content in each web page into a hierarchical structure, and an algorithm that interprets and translates these hierarchical structures into logical statements by distinguishing and representing the meta-data and their individual data instances. Findings - Experimental results for the university domain with 12 computer science department web sites, comprising 361 individual faculty and course home pages indicate that the performance of the meta-data and instance extraction averages 85, 88 percent F-measure, respectively. Our METEOR system achieves this performance without any domain specific engineering requirement. Originality/value - Important contributions of the METEOR system presented in this paper are: it performs extraction without the assumption that the object instance pages are template-driven; it is domain independent and does not require any previously engineered domain ontology; and by interpreting the link pages, it can extract both meta-data, such as concept and attribute names and their relationships, as well as their instances with high accuracy.

Original languageEnglish (US)
Pages (from-to)278-296
Number of pages19
JournalOnline Information Review
Issue number3
StatePublished - 2006


  • Data handling
  • Information retrieval
  • Worldwide web

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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