• DocumentCode
    2908336
  • Title

    Automated semantic tagging using fuzzy grammar fragments

  • Author

    Martin, Trevor ; Shen, Yun ; Azvine, B.

  • Author_Institution
    Artificial Intell. Group, Univ. of Bristol, Bristol
  • fYear
    2008
  • fDate
    1-6 June 2008
  • Firstpage
    2224
  • Lastpage
    2229
  • Abstract
    One of the bottlenecks preventing wider adoption of the semantic Web is the overhead in annotating existing Web content. In cases where we have unstructured text, it is useful to extract fragments of structured data which can then be used as the basis for automatic tagging. A common approach is to use pattern matching (e.g. regular expressions) or more general grammar-based techniques, but these are not robust against small deviations. Fuzzy grammars allow partial matches, and we outline an efficient parsing technique to determine the degree to which a string is parsed by a grammar fragment. A simple application shows the methodpsilas validity.
  • Keywords
    fuzzy reasoning; grammars; semantic Web; string matching; text analysis; automated semantic Web tagging; fuzzy grammar fragment; parsing technique; pattern matching; string matching; unstructured text; Artificial intelligence; Data mining; Dictionaries; Hidden Markov models; Humans; Natural languages; Pattern matching; Semantic Web; Tagging; Web pages;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7584
  • Print_ISBN
    978-1-4244-1818-3
  • Electronic_ISBN
    1098-7584
  • Type

    conf

  • DOI
    10.1109/FUZZY.2008.4630678
  • Filename
    4630678