@inproceedings{da08d009b70f40adb8bfcd7d58818238,
title = "Knowledge-based Transfer Learning Explanation",
abstract = "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.",
keywords = "ontology, transfer learning, description logic, explanative AI",
author = "Jiaoyan Chen and Freddy Lecue and Pan, {Jeff Z} and Ian Horrocks and Huajun Chen",
note = "The work was partially funded by the research center SIRIUS, the EPSRC project DBOnto, the EU Marie Curie KDrive project (286348) and NSFC61673338.; 16th International Conference on Principles of Knowledge Representation and Reasoning, (KR2018) ; Conference date: 30-10-2018 Through 02-11-2018",
year = "2018",
month = sep,
day = "24",
language = "English",
isbn = "9781577358039",
publisher = "AAAI Press",
pages = "349--358",
editor = "Thielscher, {Michael } and Francesca Toni and Frank Wolter",
booktitle = "Principles of Knowledge Representation and Reasoning",
}