Sentence examples for target instance from inspiring English sources

Given a schema mapping, we then provide an algorithm to construct a canonical target instance.

This article investigates the construction of target instance for XML data exchange, which has received far less attention.

Moreover, we develop techniques to enforce non-key constraints on the canonical target instance, by providing a chase method to reason about data.

The higher the β, the more similar an unlabeled target instance is.

Using Pearson correlation [20], the similarity is measured between the new arriving target instance and its co-occurred counterpart.

Therefore, combining these two measures will provide us with the similarity of the arriving target instance with the source from a different feature space.

Experimental results show that our algorithms scale well, and are effective in producing target instances of good quality.

The details of the number of source and target instances as well as the number of accepted links along with the precision and recall for each is recorded in Table 2.

A learner is now trained with the transformed labeled target instances.

This process also preserves the structural consistency between the labeled and unlabeled target instances.

This method allows for multiple classes, requires limited labeled target training instances, and utilizes unlabeled target instances though one can have classes which contain only unlabeled instances.