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Filters: Author is Md Kamruzzaman Sarker  [Clear All Filters]
Conference Papers
M. K. Sarker and Hitzler, P., Efficient Concept Induction for Description Logics, in AAAI Conference on Artificial Intelligence, Honolulu, US, 2019, vol. 33.PDF icon Efficient Concept Induction for Description Logics.pdf (288.86 KB)
M. K. Sarker, Rokibul, A. Kazi Md, and Arifuzzaman, M., Emotion recognition from speech based on relevant feature and majority voting, in ICIEV, 2014.PDF icon 06850685.pdf (675.21 KB)
A. Eberhart, Shimizu, C., Chowdhury, S., Sarker, M. K., and Hitzler, P., Expressibility of OWL Axioms with Patterns, in ESWC 2021, In Press.PDF icon Expressibility_of_OWL_ Axioms_with_Patterns.pdf (357.95 KB)
M. K. Sarker, Carral, D., Krisnadhi, A., and Hitzler, P., Modeling OWL with Rules: The ROWL Protege Plugin, Kobe, Japan, 2016.PDF icon ROWL.pdf (184.79 KB)
M. Ebrahimi, Sarker, M. K., Bianchi, F., Xie, N., Eberhart, A., Doran, D., Kim, H. S., and Hitzler, P., Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment, in AAAI-MAKE 2021, 2021.PDF icon 2021-AAAI_MAKE.pdf (753.28 KB)
M. K. Sarker, Krisnadhi, A., and Hitzler, P., OWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming, Kobe, Japan, 2016.PDF icon OWLAx.pdf (609.13 KB)
M. K. Sarker, Schwartz, J., Hitzler, P., Zhou, L., Nadella, S., Minnery, B., Juvina, I., Raymer, M. L., and Aue, W. R., Wikipedia Knowledge Graph for Explainable AI, in Second Iberoamerican Knowledge Graphs and Semantic Web Conference (KGSWC), 2020.PDF icon 2020_Wiki_KG_kgswc_conference.pdf (1.02 MB)
Conference Proceedings
M. K. Sarker, Xie, N., Doran, D., Raymer, M., and Hitzler, P., Explaining Trained Neural Networks with Semantic Web Technologies: First Steps, Twelveth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy. London, UK, 2017.PDF icon 2017-nesy-xai.pdf (2.09 MB)
N. Xie, Sarker, M. K., Doran, D., Hitzler, P., and Raymer, M., Relating Input Concepts to Convolutional Neural Network Decisions, NIPS 2017 Workshop: Interpreting, Explaining and Visualizing Deep Learning, NIPS IEVDL 2017. NIPS, CA, USA, 2017.PDF icon Relating Input Concepts to Convolutional Neural Network Decisions.pdf (2.35 MB)