Leveraging Linked Data to Infer Semantic Relations within Structured Sources.
Mohsen Taheriyan, Craig A. Knoblock, Pedro A. Szekely, José Luis Ambite, Yinyi Chen · 2015
Abstract. Information sources such as spreadsheets and databases con-tain a vast amount of structured data. Understanding the semantics of this information is essential to automate searching and integrating it. Se-mantic 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., an-notating the first column of a table with dbpedia:Person and the second one with dbpedia:Film. However, a precise semantic model needs to ex-plicitly 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 oc-curring in the Linked Open Data (LOD) to automatically infer the se-mantic 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. Min-ing 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.