Abstract
This paper presents a process for leveraging structural relationships and reusable phrases when translating large-scale ontologies. Digital libraries are becoming more and more prevalent.
An important step in providing universal access to such material is to provide multi-lingual access to the underlying principles of organization via ontologies, thesauri, and controlled vocabularies. Machine translation of these resources requires high accuracy and a deep vocabulary.
Human input is often required, but full manual translation can be slow and expensive. We report on a cost-effective approach to ontology translation.
We describe our technique of prioritization, our process of collecting aligned translations and generating a new lexicon, and the resulting improvement to translation system output. Our preliminary evaluation indicates that this technique provides significant cost savings for human-assisted translation.
The process we developed can be applied to ontologies in other domains and is easily inc