METEOR: metadata and instance extraction from object referral lists on the web

ABSTRACT

The Web has established itself as the largest public data repository ever available. Even though the vast majority of information on the Web is formatted to be easily readable by the human eye, "meaningful information" is still largely inaccessible for the computer applications. In this paper we present the METEOR system which utilizes various presentation and linkage regularities from referral lists of various sorts to automatically separate and extract metadata and instance information. 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 metadata and instance extraction averages 85%, 88% F-measure respectively. METEOR achieves this performance without any domain specific engineering requirement.

REFERENCES

• 1: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 2: Michael Kifer , Georg Lausen , James Wu, Logical foundations of object-oriented and frame-based languages, Journal of the ACM (JACM), v.42 n.4, p.741-843, July 1995 [doi>10.1145/210332.210335]
• 3: G. Yang, W. Tan, S. Mukherjee, I.V.Ramakrishnan, and H. Davulcu. On the power of semantic partitioning of web documents. In Workshop on Information Integration on the Web, Acapulco, Mexico, 2003.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.m Miscellaneous

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

General Terms:
Algorithms, Experimentation, Performance

Keywords:
extraction, instance, metadata, object, semantic, web