The OntoLex-Lemon model provides a vocabulary to enrich ontologies with linguistic information that can be exploited by Natural Language Processing applications. The increasing uptake of Lemon illustrates the growing interest in combining linguistic information and Semantic Web technologies. In this paper, we present Fuzzy Lemon, an extension of Lemon that allows to assign an uncertainty degree to lexical semantic relations. Our approach is based on an OWL ontology that defines a hierarchy of data properties encoding different types of uncertainty. We also illustrate the usefulness of Fuzzy Lemon by showing that it can be used to represent the confidence degrees of automatically discovered translations between pairs of bilingual dictionaries from the Apertium family.

In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages. We rst introduce the goals of cross-lingual linking and associated technologies, and in particular, the role that the Linked Data paradigm (Bizer et al., 2011) applied to language data can play in this context. We de ne under-resourced languages with a speci c focus on languages actively used on the internet, i.e., languages with a digitally versatile speaker community, but limited support in terms of language technology. We argue that languages for which considerable amounts of textual data and (at least) a bilingual word list are available, techniques for cross-lingual linking can be readily applied, and that these enable the implementation of downstream applications for under-resourced languages via the localisation and adaptation of existing technologies and resources.