@incollection{LuengenBeisswengerSelzametal.2016, author = {Harald L{\"u}ngen and Michael Bei{\"s}wenger and Bianca Selzam and Angelika Storrer}, title = {Modelling and Processing Wordnets in OWL}, series = {Modelling, Learning, and Processing of Text-Technological Data Structures}, editor = {Alexander Mehler and Kai-Uwe K{\"u}hnberger and Henning Lobin and Harald L{\"u}ngen and Angelika Storrer and Andreas Witt}, publisher = {Springer}, address = {Berlin/Heidelberg}, isbn = {978-3-642-22612-0}, url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-48322}, pages = {347 -- 376}, year = {2016}, abstract = {In this contribution, we discuss and compare alternative options of modelling the entities and relations of wordnet-like resources in the Web Ontology Language OWL. Based on different modelling options, we developed three models of representing wordnets in OWL, i.e. the instance model, the dass model, and the metaclass model. These OWL models mainly differ with respect to the ontological Status of lexical units (word senses) and the synsets. While in the instance model lexical units and synsets are represented as individuals, in the dass model they are represented as classes; both model types can be encoded in the dialect OWL DL. As a third alternative, we developed a metaclass model in OWL FULL, in which lexical units and synsets are defined as metaclasses, the individuals of which are classes themselves. We apply the three OWL models to each of three wordnet-style resources: (1) a subset of the German wordnet GermaNet, (2) the wordnet-style domain ontology TermNet, and (3) GermaTermNet, in which TermNet technical terms and GermaNet synsets are connected by means of a set of “plug-in” relations. We report on the results of several experiments in which we evaluated the performance of querying and processing these different models: (1) A comparison of all three OWL models (dass, instance, and metaclass model) of TermNet in the context of automatic text-to-hypertext conversion, (2) an investigation of the potential of the GermaTermNet resource by the example of a wordnet-based semantic relatedness calculation.}, language = {en} }