Real world semantic web applications by Kashyap V (eds )

By Kashyap V (eds )

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Semantic) metadata. Extraction of semantic metadata not only includes identification of relevant entities but also relationships within the context of relevant ontology. This paper describes SEE's architecture, which provides a common API for heterogeneous document processing, with discrete, reusable and highly configurable modular components. This results in exceptional flexibility, extensibility and performance. Referred to as SEE modules (SEEMs), which are divided along functional lines, these processors perform one of the following roles: restriction (determine the segments of the input text to operate upon); enhancement (discover textual features of semantic interest); filtering (augment, remove or supplement the features recognized); or outputting (generate reports, annotate the original, update databases, or other actions).

C. Dubes. Algorithms for Clustering Data. , 1988. [6] J. Lee, M. Kim, and Y. Lee. Information retrieval based on conceptual distance in isa hierarchies. Journal of Documentation, 49:188–207, 1993. [7] Oliver Mason. Qtag: A portable parts of speech tagger. uk/QTAG/, 1998. [8] G. Miller, C. Leacock, T. Randee, and R. Bunker. A semantic concordance. In proceedings of the 3rd DARPA Workshop on Human Language Technology, pages 303-308, 1993. [9] J. Morris and G. Hirst. Lexical cohesion, the thesaurus, and the structure of text.

When identifying DocumentFeatures, some false positives can be avoided by using endof-sentence and end-of-phrase information. SEE currently uses a number of heuristics based primarily on punctuation and capitalization to detect breaks corresponding to sentence and phrase boundaries. Though currently designed for English syntax, the SEE sentence/phrase analyzer is being enhanced to support other languages; this is made easier by its object-oriented design. This can be contrasted with systems that use table-driven parsing.

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