Publications

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OWL EL
R. Mutharaju, Hitzler, P., and Mateti, P., “Distributed OWL EL Reasoning: The Story So Far”, in Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy, Riva del Garda, Italy, 2014, vol. 1261, pp. 61-76.PDF icon Distributed-EL-Story-SSWS2014.pdf (437.93 KB)
R. Mutharaju, Hitzler, P., Mateti, P., and Lécué, F., “Distributed and Scalable OWL EL Reasoning”, in Proceedings of the 12th Extended Semantic Web Conference (ESWC 2015) , Portoroz, Slovenia, 2015.PDF icon DistEL-ESWC2015.pdf (330.04 KB)
Paraconsistency
F. Maier, Ma, Y., and Hitzler, P., “Paraconsistent OWL and Related Logics”, Semantic Web, vol. 4, pp. 395–427, 2013.PDF icon paraOWL-2013.pdf (698.73 KB)
Paraconsistent reasoning
S. Huang, Li, Q., and Hitzler, P., “Reasoning with Inconsistencies in Hybrid MKNF Knowledge Bases”, Logic Journal of the IGPL, vol. 21, pp. 263–290, 2013.PDF icon ljigpl-parahkmf-2013.pdf (225.39 KB)
Part of Relation
P. Jain, Hitzler, P., Verma, K., Yeh, P. Z., and Sheth, A., “Moving beyond SameAs with PLATO: Partonomy detection for Linked Data”, in 23rd ACM Conference on Hypertext and Social Media, HT '12, Milwaukee, WI, USA, 2012, pp. 33–42.PDF icon plato-ht2012.pdf (314.03 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)
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)
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)
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)
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)
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)
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)
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)
Reasoning
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)
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.

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