Publications

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refinement operators
J. Lehmann and Hitzler, P., “Concept learning in description logics using refinement operators”, Machine Learning, vol. 78, pp. 203–250, 2010.PDF icon lehmann-hitzler-MLJ-2010.pdf (1.05 MB)
Recurrent neural networks
B. Hammer, Hitzler, P., Maass, W., and Toussaint, M., “10302 Abstracts Collection - Learning paradigms in dynamic environments”, Learning paradigms in dynamic environments. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany, Dagstuhl, Germany, 2010.
Recurrent Neural Network
M. Sen Sarma and Das, A., “BMGC: A Deep Learning Approach to Classify Bengali Music Genres”, in Proceedings of the 4th International Conference on Networking, Information Systems & Security, New York, NY, USA, 2021.
Reasoning
D. Carral, “Efficient Reasoning Algorithms for Fragments of Horn Description Logics”, Wright State University, Dayton, 2017.PDF icon PhD Dissertation Carral.pdf (578.29 KB)
A. Eberhart, Ebrahimi, M., Zhou, L., Shimizu, C., and Hitzler, P., “Completion Reasoning Emulation for the Description Logic EL+”, Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice, vol. 2600. CEUR-WS.org, Stanford University, Palo Alto, California, USA, 2020.PDF icon paper5.pdf (1.89 MB)
RDF reification
S. S. Sahoo, Bodenreider, O., Hitzler, P., Sheth, A., and Thirunarayan, K., “Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data”, in Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010, Heidelberg, Germany, 2010, vol. 6187, pp. 461–470.PDF icon PaCE-SSDBM2010.pdf (810.08 KB)
RCC5
J. Zalewski, Hitzler, P., and Janowicz, K., “Semantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)”, 2021.PDF icon Semantic Compression Technical Report (354.64 KB)
J. Zalewski, Hitzler, P., and Janowicz, K., “Semantic Compression with Region Calculi in Nested Hierarchical Grids”, in Proceedings of the 29th International Conference on Advances in Geographic Information Systems, New York, NY, USA, 2021, pp. 305–308.PDF icon semantic_compression.pdf (444.8 KB)
random generation
A. Eberhart, Cheatham, M., and Hitzler, P., “Pseudo-Random ALC Syntax Generation”, in The Semantic Web: ESWC 2018 Satellite Events - ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers, Heraklion, Crete, Greece, 2018, vol. 11155, pp. 19–22.PDF icon Pseudo_Random_ALC_Syntax_Generation.pdf (134.95 KB)
Provenir ontology
S. S. Sahoo, Bodenreider, O., Hitzler, P., Sheth, A., and Thirunarayan, K., “Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data”, in Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010, Heidelberg, Germany, 2010, vol. 6187, pp. 461–470.PDF icon PaCE-SSDBM2010.pdf (810.08 KB)
Provenance Management Framework.
S. S. Sahoo, Bodenreider, O., Hitzler, P., Sheth, A., and Thirunarayan, K., “Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data”, in Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010, Heidelberg, Germany, 2010, vol. 6187, pp. 461–470.PDF icon PaCE-SSDBM2010.pdf (810.08 KB)
Provenance context entity
S. S. Sahoo, Bodenreider, O., Hitzler, P., Sheth, A., and Thirunarayan, K., “Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data”, in Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010, Heidelberg, Germany, 2010, vol. 6187, pp. 461–470.PDF icon PaCE-SSDBM2010.pdf (810.08 KB)
Propositional Logic
M. Labaf, “ Propositional rule extraction from neural networks under background knowledge”, in Twelfth International Workshop on Neural-Symbolic Learning and Reasoning, 2017.PDF icon main.pdf (270.11 KB)
Prediction
A. Hossein Yazdavar, Mahdavinejad, M. Saeid, Baja, G., Romine, W., Sheth, A., Monadjemi, A. Hassan, Thirunarayan, K., Meddar, J. M., Myers, A., Pathak, J., and Hitzler, P., “Multimodal mental health analysis in social media”, PLoS ONE, 2020.PDF icon journal.pone_.0226248-min.pdf (1007.16 KB)
Pose estimation
S. Salam Aonty, Deb, K., Sarma, M. Sen, Dhar, P. Kumar, and Shimamura, T., “Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model”, IEEE Access, vol. 11, pp. 42343-42360, 2023.

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