Augmenting Transfer Learning with Semantic Reasoning

Freddy Lécué, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19
EditorsSarit Kraus
PublisherInternational Joint Conferences on Artificial Intelligence Organization
Number of pages7
ISBN (Electronic)978-0-9992411-4-1
Publication statusPublished - 31 Aug 2019


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