Leveraging Linked Data to Infer Semantic Relations within Structured Sources

Information sources such as spreadsheets and databases contain a vast amount of structured data. Understanding the semantics of this information is essential to automate searching and integrating it. Semantic models capture the intended meaning of data sources by mapping them to the concepts and relationships defined by a domain ontology. Most of the effort to automatically build semantic models is focused on labeling the data fields with ontology classes and/or properties, e.g., annotating the first column of a table with dbpedia:Person and the second one with dbpedia:Film. However, a precise semantic model needs to explicitly represent the relationships too, e.g., stating that dbpedia:director is the relation between the first and second column. In this paper, we present a novel approach that leverages the small graph patterns occurring in the Linked Open Data (LOD) to automatically infer the semantic relations within a given data source assuming that the source attributes are already annotated with semantic labels. We evaluated our approach on a dataset of museum sources using the linked data published by Smithsonian American Art Museum as background knowledge. Mining only patterns of length one and two, our method achieves an average precision of 78% and recall of 70% in inferring the relationships included in the semantic models associated with data sources.