Knowledge portability with semantic expansion of ontology labels

Our research focuses on the multilingual enhancement of ontologies that, often represented only in English, need to be translated in different languages to enable knowledge access across languages. Ontology translation is a rather different task then the classic document translation, because ontologies contain highly specific vocabulary and they lack contextual information. For these reasons, to improve automatic ontology translations, we first focus on identifying relevant unambiguous and domain-specific sentences from a large set of generic parallel corpora. Then, we leverage Linked Open Data resources, such as DBPedia, to isolate ontologyspecific bilingual lexical knowledge. In both cases, we take advantage of the semantic information of the labels to select relevant bilingual data with the aim of building an ontology-specific statistical machine translation system. We evaluate our approach on the translation of a medical ontology, translating from English into German. Our experiment shows a significant improvement of around 3 BLEU points compared to a generic as well as a domain-specific translation approach.This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight) and the European Union supported projects LIDER (ICT-2013.4.1-610782) and MixedEmotions (H2020-644632)