Abstract. Linked data continues to grow at a rapid rate, but a limitation of a lot of the data that is being published is the lack of a semantic description. There are tools, such as D2R, that allow a user to quickly convert a database into RDF, but these tools do not provide a way to easily map the data into an existing ontology. This paper presents a semiautomatic approach to map structured sources to ontologies in order to build semantic descriptions (source models). Since the precise mapping is sometimes ambiguous, we also provide a graphical user interface that allows a user to interactively refine the models. The resulting source models can then be used to convert data into RDF with respect to a given ontology or to define a SPARQL end point that can be queried with respect to an ontology. We evaluated the overall approach on a variety of sources and show that it can be used to quickly build source models with minimal user interaction.
Recent approaches have been studied in Big Data integration, amongst which we can cite the work in REF that presents a semiautomatic approach to map structured sources to ontologies in order to build semantic descriptions (source models).
Semi-Automatically Mapping Structured Sources into the Semantic Web ⋆
ESWC
Computer Science