2011م - 1444هـ
Experiments with Term Translation



Our research on the translation of ontology vocabularies is motivated by the challenge of translating

domain-specific terms with restricted or no additional textual context that in other cases may be

used to improve the translation. For our experiment we started by translating financial terms with

the baseline systems trained on the JRC-Acquis (Steinberger et al., 2006) corpus and the European

Central Bank Corpus (Tiedemann, 2009). Although both resources contain a large amount of

parallel data, the translations were not satisfactory. To improve the translations of the financial

ontology vocabulary we built a new parallel resource, which was generated using Linguee, an online

translation query service. With this data, we could train a small model, which produced better

translations than the baseline model using only general resources.

Since the manual development of terminological resources is a time intensive and expensive task,

we used Wikipedia as a background knowledge base and examined the articles tagged with domainspecific categories. With this extracted domain-specific data we built a specialised English-German

lexicon to store translations of domain-specific terms. These terms were then used in a pre-processing

method in the decoding approach. This approach incorporates the work by (Aggarwal et al., 2011),

where the authors use the ontology structure to calculate the similarity between the labels. They

combine the semantic, terminological and linguistic information for monolingual ontology matching,

which can be extended to the multilingual scenario. We split the financial terms into n-grams and

queried for financial sub-terms in Wikipedia, which we used to query Wikipedia.