Augmenting Transfer Learning with Semantic Reasoning
This work addresses the challenge of enhancing transfer learning efficiency for real-world forecasting tasks, though it appears incremental as it builds on existing methods.
The paper tackles the problem of improving transfer learning by incorporating semantic reasoning, specifically addressing when and what to transfer using semantic measurements and embeddings, and demonstrates robustness with applications in bus delay and air quality forecasting.
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.