Title :
Automated semantic tagging using fuzzy grammar fragments
Author :
Martin, Trevor ; Shen, Yun ; Azvine, B.
Author_Institution :
Artificial Intell. Group, Univ. of Bristol, Bristol
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;
Conference_Titel :
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1818-3
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2008.4630678