Opaque Attribute Alignment by Jennifer Sleeman, Rafael Alonso, Hua Li, Art Pope, and Antonio Badia.
Abstract:
Ontology alignment describes a process of mapping ontological concepts, classes and attributes between different ontologies providing a way to achieve interoperability. While there has been considerable research in this area, most approaches that rely upon the alignment of attributes use label based string comparisons of property names. The ability to process opaque or non-interpreted attribute names is a necessary component of attribute alignment. We describe a new attribute alignment approach to support ontology alignment that uses the density estimation as a means for determining alignment among objects. Using the combination of similarity hashing, Kernel Density Estimation (KDE) and Cross entropy, we are able to show promising F-Measure scores using the standard Ontology Alignment Evaluation Initiative (OAEI) 2011 benchmark.
Just in case you run across different ontologies covering the same area, however unlikely that seems 10+ years after the appearance of the Semantic Web.