Using a Natural Language Understanding System to Generate Semantic Web Content

Using a Natural Language Understanding System to Generate Semantic Web Content

Akshay Java (University of Maryland-Baltimore County, USA), Sergei Nirneburg (University of Maryland-Baltimore County, USA), Marjorie McShane (University of Maryland-Baltimore County, USA), Timothy Finin (University of Maryland-Baltimore County, USA), Jesse English (University of Maryland-Baltimore County, USA) and Anupam Joshi (University of Maryland-Baltimore County, USA)
Copyright: © 2007 |Pages: 25
DOI: 10.4018/jswis.2007100103
OnDemand PDF Download:
$37.50

Abstract

We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over 15 years, uses a specially constructed NLP-oriented ontology and an ontological-semantic lexicon to translate English text into a custom ontology- motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an experimental Web service that monitors RSS news sources, processes the summaries of the news stories, and publishes a structured representation of the meaning of the text in the news story.

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing