Cross-Language Name Matching for Data Fusion in Linked Open Data

Data quality and accuracy affects the success of data integration in Linked Open Data (LOD). The main goal of data fusion is to represent each real-world entity once on the Web. Data inaccuracy problems exist due to misspelling and a wide range of typographical differences mainly in non-Latin languages, those problems become more complicated when a person is identified by a name, and this name can be presented differently in same/different languages. Up to author's knowledge, the previous approaches which supported Arabic person names are not designed to work with LOD. This paper proposes a framework that uses person names as matching criteria from cross-language LOD Datasets. The proposed framework has substantial improvements in matching results compared to state of the art framework of matching techniques with better matching rate which exceed 6% in precision and 6% in recall.