Schema Evolution publication categorizer

Ontologies play a key role in the advent of the Semantic Web. An important problem when dealing with ontologies is the modification of an existing ontology in response to a certain need for change. This problem is a complex and multifaceted one, because it can take several different forms and includes several related subproblems, like heterogeneity resolution or keeping track of ontology versions. As a result, it is being addressed by several different, but closely related and often overlapping research disciplines.

This paper introduces the first formal framework for learning mappings
between heterogeneous schemas which is based on logics and probability
theory. This task, also called “schema matching”, is a crucial step in integrating
heterogeneous collections. As schemas may have different granularities, and as
schema attributes do not always match precisely, a general-purpose schema mapping
approach requires support for uncertain mappings, and mappings have to be
learned automatically. The framework combines different classifiers for finding