Augmenting Transfer Learning with Semantic Reasoning

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

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

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.
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
Pages1779-1785
Number of pages7
ISBN (Electronic)9780999241141
ISBN (Print)9780999241141
DOIs
Publication statusPublished - 2019
EventTwenty-Eighth International Joint Conference on Artificial Intelligence - Macao, China
Duration: 10 Aug 201916 Aug 2019

Conference

ConferenceTwenty-Eighth International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2019
CountryChina
CityMacao
Period10/08/1916/08/19

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Lecue, F., Chen, J., Pan, J. Z., & Chen, H. (2019). Augmenting Transfer Learning with Semantic Reasoning. In S. Kraus (Ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI2019) (pp. 1779-1785). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/246