Holistic and Scalable Ontology Alignment for Linked Open Data

The Linked Open Data community continuously releases massive amounts of RDF data that shall be used to easily create applications that incorporate data from different sources. Inter-operability across different sources requires links at instanceand at schema-level, thus connecting entities on the one hand and relating concepts on the other hand. State-of-the-art entityand ontology-alignment methods produce high quality alignments for two “nicely structured” individual sources, where an identification of relevant and meaningful pairs of ontologies is a precondition. Thus, these methods cannot deal with heterogeneous data from many sources simultaneously, e.g., data from a linked open data web crawl. To this end we propose Holistic Concept Matching (HCM). HCM aligns thousands of concepts from hundreds of ontologies (from many sources) simultaneously, while maintaining scalability and leveraging the global view on the entire data cloud. We evaluated our approach against the OAEI ontology alignment benchmark as well as on the 2011 Billion Triple Challenge data and present high precision results created in a scalable manner.