  Our work presented in this paper focuses on the translation of terminological
expressions represented in semantically structured resources, like ontologies
or knowledge graphs. The challenge of translating ontology labels or
terminological expressions documented in knowledge bases lies in the highly
specific vocabulary and the lack of contextual information, which can guide a
machine translation system to translate ambiguous words into the targeted
domain. Due to these challenges, we evaluate the translation quality of
domain-specific expressions in the medical and financial domain with
statistical as well as with neural machine translation methods and experiment
domain adaptation of the translation models with terminological expressions
only. Furthermore, we perform experiments on the injection of external
terminological expressions into the translation systems. Through these
experiments, we observed a significant advantage in domain adaptation for the
domain-specific resource in the medical and financial domain and the benefit of
subword models over word-based neural machine translation models for
terminology translation.
