The approach has several advantages over
existing methods such as ease of experimentation, incoherence
mitigation during the alignment process, and
the incorporation of a-priori confidence values.
In cases
where training data is not available, weights set manually
by experts still result in improved alignment quality.
The framework is not only useful for aligning concepts and
properties but can also include instance matching. For this
purpose, one would only need to add a hidden predicate
modeling instance correspondences. The resulting matching
approach would immediately benefit from probabilistic
joint inference, taking into account the interdependencies
between terminological and instance correspondences. +
- decompose the query into simple sub-plans that can be executed by the remote endpoints.
- propose a set of physical operators that gather data generated by the endpoints, and quickly produce responses.
- an execution engine able to adapt the query execution to the availability of the endpoints and to hide delays from users. +