Augmenting Transfer Learning with Semantic Reasoning

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.

Authors

• Freddy Lécué Inria
• Jiaoyan Chen The University of Manchester
• Jeff Z. Pan The University of Edinburgh
• Huajun Chen College of Computer Science and Technology, Zhejiang University ZJU-Ant Group Joint Lab of Knowledge Graph Zhejiang Key Laboratory of Big Data Intelligent Computing

Keywords

• Knowledge Representation and Reasoning: Description Logics and Ontologies
• Machine Learning: Knowledge-based Learning
• Machine Learning: Transfer, Adaptation, Multi-task Learning