Webscaled - Dataset Marketplace
JAN 15th 2007

Future value paradigms of the Semantic Web

Published 3 years ago by James Simmons

Natural Language Processing is very important to the Semantic Web. Language processing algorithm development will rise as better and smarter NLP agents are used to scour silos of raw textual data for semantic meaning. The addition of NLP Web services to the Web will give light to new and innovative mashups. An example mashup powered could be a service that uses a language processing agent to read a news article about the Apple iPhone and:

  • Google Maps API: Provide a map of Apple store locations
  • Amazon E-Commerce API: Display product information and a link
  • Flickr API: Show pictures of the iPhone

An example of current-generation Natural Language Processing for the Semantic Web is ClearForest SWS. This service is a step in the right direction, and a preview of how NLP will be used in the Semantic Web to extract meaning from text. ClearForest SWS is still lacking in accuracy. For example, it might identify "McDonalds Big Mac" as business name, when "McDonalds" would have been correct.

Vocabularies

The extensibility of RDF comes from the ability for anyone to create their own vocabulary, thereby extending RDF to describe any conceivable information set. As the Semantic Web grows and matures, thousands of new vocabularies will appear and many will merge, split, and overlap. By utilizing common vocabularies it is possible to increase the potential for interoperability among RDF applications.

More to write about

There is much more to cover and I will continue writing on this topic in the future.

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About the author

James Simmons

It's my goal to help bring about the Semantic Web. I also like to explore related topics like natural language processing, information retrieval, and web evolution. I'm the primary author of Semantic Focus and I'm currently working on several Semantic Web projects.

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Comments for this entry:

  1. Posted by Stephen L. Reed on January 3, 2008 at 11:16am

    Hi,
    I strongly agree with the need to parse RDF from web text. My open source project has already published the largest available open-source English Lexicon, in RDF, derived from WordNet 2.1, the OpenCyc lexicon, The CMU Pronouncing Dictionary, and the English entries from Wiktionary. My goal is to build a chat system that intelligently learns new knowledge and skills, and which will ultimately gain a deep understanding of textual web resources such as Wikipedia.

    -Steve

    Stephen L. Reed
    Artificial Intelligence Researcher
    http://texai.org/blog
    http://texai.org
    3008 Oak Crest Ave.
    Austin, Texas, USA 78704
    512.791.7860

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