Knowledge-based Transfer Learning Explanation

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

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

30 Citations (Scopus)


Machine learning explanation can significantly boost machine learning's application in decision making,but the usability of current methods is limited in human-centric explanation,especially for transfer learning,an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain.In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposedand then inferred with both local ontologies and external knowledge bases.The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.
Original languageEnglish
Title of host publicationPrinciples of Knowledge Representation and Reasoning
Subtitle of host publicationProceedings of the Sixteenth International Conference (KR2018)
EditorsMichael Thielscher, Francesca Toni, Frank Wolter
Place of PublicationPalo Alto, California
PublisherAAAI Press
Number of pages10
ISBN (Print)9781577358039
Publication statusPublished - 24 Sept 2018
Event16th International Conference on Principles of Knowledge Representation and Reasoning - Tempe, United States
Duration: 30 Oct 20182 Nov 2018

Publication series

ISSN (Print)2334-1025
ISSN (Electronic)2334-1033


Conference16th International Conference on Principles of Knowledge Representation and Reasoning
Abbreviated title(KR2018)
Country/TerritoryUnited States

Bibliographical note

The work was partially funded by the research center SIRIUS, the EPSRC project DBOnto, the EU Marie Curie KDrive project (286348) and NSFC61673338.


  • ontology
  • transfer learning
  • description logic
  • explanative AI


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