Approaches to Relating and Integrating Semantic Data from Heterogeneous Sources

Integrating and relating heterogeneous data using inference is one of the cornerstones of semantic technologies and there are a variety of ways in which this may be achieved. Cross source relationships can be automatically translated or inferred using the axioms of RDFS/OWL, via user generated rules or as the result of SPARQL query result transformations. For a given problem it is not always obvious which approach (or combination of approaches) will be the most effective and few guidelines exist for making this choice. 

This paper discusses these three approaches and demonstrates them using an "acquaintance" relationship drawn from data residing in common RDF information sources such as FOAF and DBLP datastores. The implementation of each approach is described along with practical considerations for their use. Quantitative and qualitative evaluation results of each approach are presented and the paper concludes with initial suggestions for guiding principles to help in selecting an appropriate approach for integrating heterogeneous semantic data sources. cited as the biggest and most expensive challenge that information-technology organizations face, information integration is thought to consume about 40% of IT budgets [1]. 

New approaches to integration that formally represent the meaning of data in a system, offer the hope of dealing with semantic heterogeneities. Indeed, integrating and relating heterogeneous data using inference is one of the cornerstones of semantic technologies.