The main goal of this paper is to extract the semantic relations underpinning the concepts of English prepositional of-constructions derived from poetic and non-poetic datasets, using Princeton WordNet. The problem is addressed by two different algorithms, which are evaluated for their ability to model the different types of resources from which the relations are derived, and for their ability to predict unseen relations. The first algorithm introduces the concept of subsumption hierarchy between relations in order to derive the most general relations associated to each type of data source and identify a set of relations specific to each dataset. The second algorithm investigates the use of a weighting scheme in order to establish the importance of each association extracted. Of particular importance are the notions of subsumption hierarchies between relations (expressed as synset pairs) and the Inverse Relation Frequency (IRF) measure, which is inspired by the Inverse Document Frequency measure used in Information Retrieval. The ontological prospects of using Princeton WordNet and the above algorithms for the creation of ontologies are also briefly discussed. Although the main interest of the proposed methods lies to the identification of conceptual relations particular to poetic resources, the methods followed can be applied and are evaluated on other domains too.

The purpose of the current paper is to present an ontological analysis to the identification of a particular type of prepositional natural language phrases called figures of speech [1] via the identification of inconsistencies in ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to describe real world events and activities. However, one aspect that makes a prepositional noun phrase poetical is that the latter suggests a semantic relationship between concepts that does not exist in the real world. The current paper discusses how a set of rules based on Wordnet classes and an ontology representing human behavior and properties, can be used to identify figures of speech. It also addresses the problem of inconsistency resulting from the assertion of figures of speech at various levels identifying the problems involved in their representation. Finally, it discusses how a contextualized approach might help to resolve this problem.

The purpose of the current paper is to present an ontological analysis to the identification of a particular type of prepositional figures of speech via the identification of inconsistencies in ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to describe real world events and activities. However, one aspect that makes a prepositional noun phrase poetical is that the latter suggests a semantic relationship between concepts that does not exist in the real world. The current paper shows that a set of rules based on WordNet classes and an ontology representing human behaviour and properties, can be used to identify figures of speech due to the discrepancies in the semantic relations of the concepts involved. Based on this realization, the paper describes a method for determining poetic vs. non-poetic prepositional figures of speech, using WordNet class hierarchies. The paper also addresses the problem of inconsistency resulting from the assertion of figures of speech in ontological knowledge bases, identifying the problems involved in their representation. Finally, it discusses how a contextualized approach might help to resolve this problem.

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