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With the rapid increase of digitized language
content, systems for automatic processing of natural
languages and resources for such systems (such as
WordNet and FrameNet) are becoming indispensable to
business and science.
This work is a case study in creating an ontology for
the domain of Digital Identity Management, based on
ontological semantics framework and supported by
corpus evidence. The work aims to document the range
of semantic and syntactic uses of domain-specific
vocabulary, and to develop scripts for sentence
processing based on event structure theory.
The methodology describes ontology engineering that
ensures external validity and internal consistency of
the ontology and the lexicon, and aids in rapid
coverage of the lexicon of a particular domain, while
event structure-based scripts within the ontological
framework allow for processing implicature and
entailment, as well as literal language.
This analysis is useful both for computer scientists
working on natural language processing, and for
linguists interested in practical applications of
linguistic concepts and evaluation of theoretical
advances through a computational approach to natural
language processing.
content, systems for automatic processing of natural
languages and resources for such systems (such as
WordNet and FrameNet) are becoming indispensable to
business and science.
This work is a case study in creating an ontology for
the domain of Digital Identity Management, based on
ontological semantics framework and supported by
corpus evidence. The work aims to document the range
of semantic and syntactic uses of domain-specific
vocabulary, and to develop scripts for sentence
processing based on event structure theory.
The methodology describes ontology engineering that
ensures external validity and internal consistency of
the ontology and the lexicon, and aids in rapid
coverage of the lexicon of a particular domain, while
event structure-based scripts within the ontological
framework allow for processing implicature and
entailment, as well as literal language.
This analysis is useful both for computer scientists
working on natural language processing, and for
linguists interested in practical applications of
linguistic concepts and evaluation of theoretical
advances through a computational approach to natural
language processing.