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Ontological Annotation with WordNet

TitleOntological Annotation with WordNet
Publication TypeConference Paper
Year of Publication2006
AuthorsSanfilippo, A, Tratz, S, Gregory, M, Chappell, AR, Whitney, PD, Posse, C, Paulson, PR, Baddeley, B, Hohimer, RE, White, AM
EditorHandschuh, S, Declerck, T, Koivunen, M-R
Conference NameSemAnnot 2005, 5th International Workshop on Knowledge Markup and Semantic Annotation
PublisherSun SITE Central Europe Workshop Proceedings
Conference LocationGalway, Ireland

Semantic Web applications require robust and accurate annotation tools that are capable of automating the assignment of ontological classes to words in naturally occurring text (ontological annotation). Most current ontologies do not include rich lexical databases and are therefore not easily integrated with word sense disambiguation algorithms that are needed to automate ontological annotation. WordNet provides a potentially ideal solution to this problem as it offers a highly structured lexical conceptual representation that has been extensively used to develop word sense disambiguation algorithms. However, WordNet has not been designed as an ontology, and while it can be easily turned into one, the result of doing this would present users with serious practical limitations due to the great number of concepts (synonym sets) it contains. Moreover, mapping WordNet to an existing ontology may be difficult and requires substantial labor. We propose to overcome these limitations by developing an analytical platform that (1) provides a WordNet-based ontology offering a manageable and yet comprehensive set of concept classes, (2) leverages the lexical richness of WordNet to give an extensive characterization of concept class in terms of lexical instances, and (3) integrates a class recognition algorithm that automates the assignment of concept classes to words in naturally occurring text. The ensuing framework makes available an ontological annotation platform that can be effectively integrated with intelligence analysis systems to facilitate evidence marshaling and sustain the creation and validation of inference models.