With the growth of the Semantic Web and its applications, the need to use it in different languages, such as Arabic, is becoming more important. Two of the challenges with the Semantic Web technologies are the lack of multilingual support and the complexity of integrating multiple ontologies used by this technology. The objective of this paper is to present efforts that will help users who use the Arabic language to ask natural language questions and then get their semantic representation in SPARQL that allows them to be executed and get the relevant semantic results. This natural language interface makes more use of the cross-domain ontologies and hence improves the understanding of their inquiries, which is needed in some critical domains such as health and food where precise advice is essential. The approach we followed is multilingual and overcomes the limitations in the published relevant systems. With the proposed approach, users who speak Arabic can use the widely published ontologies in English without concern for the translation of their questions. The proposed approach will take care of matching the entered questions with the relevant ontologies to produce their semantic Web queries. The proposed approach has been implemented and empirically evaluated. The experimental results are promising, which will help in improving the awareness and usage of the Semantic Web by different lingual and cultural users.
['Ahmed Al-Nazer', 'Saeed Albukhitan', 'Tarek Helmy']
Cross-Domain Semantic Web Model for Understanding Multilingual Natural Language Queries: English/Arabic Health/Food Domain Use Case.