MONTAGE: CREATING SELF-POPULATING DOMAIN ONTOLOGIES FROM LINKED OPEN DATA

Abstract

Domain-specific ontologies have become integral components of numerous semantic- and knowledge-based applications. However, creating such ontologies and populating them with correct individuals is a difficult and time-consuming process. Recently, a vast amount of knowledge has become available as part of the Linked Open Data (LOD) project, which includes data sets in multiple areas. In this paper, we present mOntage, a novel ontology design and population framework, which allows a domain expert to easily define a domain ontology schema and specify the ontology's classes and properties in terms of the subsets of the existing LOD data sources. The classes and properties of the ontology being created can be defined either directly, in terms of existing LOD-available classes and properties, or can be newly constructed by the domain expert. The definitions, called maps, are encoded as part of the ontology itself, effectively converting it into a self-populating ontology. Finally, a dedicated software system automatically populates the ontology with instances obtained from the selected LOD sources by executing suitable SPARQL queries. We illustrate our framework by creating Cancer Treatment ontology in the area of biomedicine.