Augmenting Transfer Learning with Semantic Reasoning

Freddy Lecue, Jiaoyan Chen, Jeff Z Pan, Huajun Chen

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

4 Citations (Scopus)


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 28th International Joint Conference on Artificial Intelligence (IJCAI2019)
EditorsSarit Kraus
PublisherAAAI Press/International Joint Conferences on Artificial Intelligence
Number of pages7
ISBN (Electronic)9780999241141
ISBN (Print)9780999241141
Publication statusPublished - 1 Aug 2019
EventTwenty-Eighth International Joint Conference on Artificial Intelligence - Macao, China
Duration: 10 Aug 201916 Aug 2019


ConferenceTwenty-Eighth International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2019

Bibliographical note

This work is partially funded by NSFC91846204.


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