期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：12
页码：2761
出版社：Journal of Theoretical and Applied
摘要：Search engines such Yahoo and Google among others has played significant role Web data access. However these search engines has limitations. These search engines are based on a keyword search which lacks semantics in the retrieval process. To cope with the Limitations of current search engines, Semantic Web was introduced. Semantic Web enables retrieval of data on the Web semantically. In semantic Web, data is standardised in a format that enables retrieval of such data semantically. But Semantic Web also has challenges where retrieval requires complex structured query such as SPARQL which is not simple are using Google like natural language query. This paper presents an approach of automatic semantic query formulation that enables retrieval of semantically structured data using natural language. The proposed approach is based on using machine learning and the result has shown improvement of 17.4% compared to existing approach in FREyA in terms of effectiveness formulated natural language queries to structured query.
