


The resulting ontology was demonstrated to be reliable and enriched. Through FOCA-based validation results, the proposed method was shown to be e®ective and e±cient. The method of the NeOn-based collaborations among domain experts and ontology engineers was conducted to build up the ViLO ontology. The ViLO ontology mainly consists of related institutions of Vietnamese political system, types and structures of legal documents. This study proposes a core ontology for Vietnamese legal documents which covers general legal domain called as ViLO. With the needs for legal information management in smart applications, especially for Vietnamese law, it is vitally important to construct core legal ontologies for knowledge representation. Innovative systems and ontologies in the law hold potential to conduct legal research. Legal ontologies play a key role in various legal applications and have been broadly used by many stakeholders. The benefit of the proposed method includes a short translation time and fewer mistranslations. The findings of the experiment show the effectiveness of the proposed method in achieving the design expectation. The objective of feature transfer learning is to reuse the past knowledge obtained in the form of dataset to be utilized for another target data.

On the basis of preprocessing English-Chinese translation text data, features of English-Chinese translation text are extracted, features of English-Chinese translation text are rapidly classified by feature transfer learning, and machine models of English-Chinese translation are constructed based on the classification results. On the basis of analyzing the basic principles and specific strategies of English-Chinese machine translation, the traditional neural machine translation methods are analyzed, and then the translation process is optimized by transfer learning.
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