A unified approach to matching semantic data on the Web

In recent years, the Web has evolved from a global information space of linked documents to a space where data are linked as well. The Linking Open Data (LOD) project has enabled a large number of semantic datasets to be published on the Web. Due to the open and distributed nature of the Web, both the schema (ontology classes and properties) and instances of the published datasets may have heterogeneity problems. In this context, the matching of entities from different datasets is important for the integration of information from different data sources. Recently, much work has been conducted on ontology matching to resolve the schema heterogeneity problem in the semantic datasets. However, there is no unified framework for matching both schema entities and instances. This paper presents a unified matching approach to finding equivalent entities in ontologies and LOD datasets on the Web. The approach first combines multiple lexical matching strategies using a novel voting-based aggregation method; then it utilizes the structural information and the already found correspondences to discover additional ones. We evaluated our approach using datasets from both OAEI and LOD. The results show that the voting-based aggregation method provides highly accurate matching results, and that the structural propagation procedure effectively improves the recall of the results.