While linked data is frequently independent of language, the interpretation of this data requires natural language identifiers in order to be meaningful to the end user. For many applications of linked data, especially in multilingual contexts, it is necessary to go beyond the simple string label and provide a richer description of the lexicalization of the linked data entities, for example by generating natural language descriptions of the data. To address this gap we have proposed a model, which we call Lemon (Lexicon Model for Ontologies), that distinguishes the labels at both the semantic and syntactic levels. Lemon aims to build on existing models for representing lexical information, but is concise, descriptive and modular. Furthermore, this model is designed to bridge the gap between the existing linked data cloud, described in formats such as RDF(S) and OWL and the rapidly growing linguistic linked data cloud, where a significant amount of multilingual data already exists. I will show examples of how we can use collaborative editing techniques with the Lemon model to create such data without significant effort and how this can be applied to tasks such as answering natural language questions over linked data.

The transcript of the Q&A session "Linked Open Data and the Lexicon" is available here.