Abstract
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