TY - GEN
T1 - Ontology-based Interpretable Machine Learning for Textual Data
AU - Lai, Phung
AU - Phan, Nhat Hai
AU - Hu, Han
AU - Badeti, Anuja
AU - Newman, David
AU - Dou, Dejing
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is a major problem of long and complicated text data, we design a learnable anchor algorithm, to better extract explanations locally. A set of regulations is further introduced, regarding combining learned interpretable representations with anchors to generate comprehensible semantic explanations. An extensive experiment conducted on two real-world datasets shows that our approach generates more precise and insightful explanations compared with baseline approaches.
AB - In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is a major problem of long and complicated text data, we design a learnable anchor algorithm, to better extract explanations locally. A set of regulations is further introduced, regarding combining learned interpretable representations with anchors to generate comprehensible semantic explanations. An extensive experiment conducted on two real-world datasets shows that our approach generates more precise and insightful explanations compared with baseline approaches.
KW - anchor
KW - information extraction
KW - interpretable machine learning
KW - natural language processing
KW - ontology
UR - http://www.scopus.com/inward/record.url?scp=85093817933&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85093817933&partnerID=8YFLogxK
U2 - 10.1109/IJCNN48605.2020.9206753
DO - 10.1109/IJCNN48605.2020.9206753
M3 - Conference contribution
AN - SCOPUS:85093817933
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020
Y2 - 19 July 2020 through 24 July 2020
ER -