The Linking Open Data (LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 31billion RDF triples. Accessing linked data is a challenging task because each data set in the LOD cloud has a specific ontology schema, and familiarity with the ontology schema used is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experiments show that our Mid-Ontology learning approach successfully integrates diverse ontology schema with a high quality, and effectively retrieves related information with the constructed Mid-Ontology.
2013 The Institute of Electronics, Information and Communication Engineers