While the multilingual data on the Semantic Web grows rapidly, the building of multilingual ontologies from monolingual ones is still cumbersome and hampered due to the lack of techniques for cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates the semantic interoperability between different ontologies in different natural languages. Achieving such enrichment by human labor is very costly and error-prone. Thus, in this paper, we propose a fully automated ontology enrichment approach (OECM), which builds a multilingual ontology by enriching a monolingual ontology from another one in a different natural language, using a cross-lingual matching technique. OECM selects the best translation among all available translations of ontology concepts based on their semantic similarity with the target ontology concepts. We present a use case of our approach for enriching English Scholarly Communication Ontologies using German and Arabic ontologies from the MultiFarm benchmark. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2018). Our approach has higher precision and recall in comparison to five state-of-the-art approaches. Additionally, we recommend some linguistic corrections in the Arabic ontologies in Multifarm which have enhanced our cross-lingual matching results.
