  Transfer learning aims at building robust prediction models by transferring
knowledge gained from one problem to another. In the semantic Web, learning
tasks are enhanced with semantic representations. We exploit their semantics to
augment transfer learning by dealing with when to transfer with semantic
measurements and what to transfer with semantic embeddings. We further present
a general framework that integrates the above measurements and embeddings with
existing transfer learning algorithms for higher performance. It has
demonstrated to be robust in two real-world applications: bus delay forecasting
and air quality forecasting.
