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Semantic Web ontologies are being increasingly used in modern text analytics applications and ontology-based information extraction (OBIE) as a means to provide a semantic backbone either for modelling the internal conceptual data structures of the text analytics (TA) engine or to model the knowledge base to drive the analysis of unstructured information in raw text and subsequent Knowledge acquisition and population. creating and targeting language resources (LR)s from a TA to an ontology can be time consuming and costly.The authors describe a user-friendly method for ontology engineers to augment an ontologies with a lexical layer which provides a flexible framework to identify term mentions of ontology concepts in raw text. In this paper we explore multilinguality in these lexical layers using the same framework. We discuss a number of potential issues for the ldquolinguistic lightrdquo lexical extensions for ontologies (LEON) approach when looking at languages more morphologically rich and which have more complex linguistic constraints than English. We show how the LEON approach can cope with these phenomena once the morphological normaliser used in the lexical analysis process is able to generalise sufficiently well for the language concerned.