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Title: Extraction of Poetic and Non-Poetic relations from of-Prepositions using WordNet
Authors: Panayiotou, Christiana 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Semantic relations extraction;Ontology learning;NLP;Knowledge representation
Issue Date: 2022
Source: IEEE Access, 2020, vol. 10, pp. 3469-3494
Volume: 10
Start page: 3469
End page: 3494
Journal: IEEE Access 
Abstract: 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.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3140030
Rights: This work is licensed under a Creative Commons Attribution 4.0 License.
Type: Article
Affiliation : Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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