LEAP learns both the structure and the parameters of DISPONTE KBs exploiting EDGE.
LEAP is based on the system CELOE for ontology engineering and exploits its search strategy in the space of possible axioms. LEAP uses the axioms returned by CELOE to build a KB so that the likelihood of the examples is maximized. LEAP also contains LEAPMR, which exploits EDGEMR to parallelize part of the learning process.
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Source Code
The last version of LEAP is available in the DL-Learner framework. The code can be downloaded from GitHub here.
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Bibliography
Giuseppe Cota. Inference and Learning Systems for Uncertain Relational Data, volume 35 of Studies on the Semantic Web. IOS Press, 2018. [ DOI | http ]
Riccardo Zese. Probabilistic Semantic Web, volume 28 of Studies on the Semantic Web. IOS Press, 2017. [ bib | DOI | http ]
Giuseppe Cota, Riccardo Zese, Elena Bellodi, Evelina Lamma, and Fabrizio Riguzzi. Learning probabilistic ontologies with distributed parameter learning. In Proceedings of the Doctoral Consortium (DC) co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015), volume 1485 of CEUR Workshop Proceedings, pages 7-12, Aachen, Germany, 2015. © by the authors, Sun SITE Central Europe. [ bib | .pdf ]
Giuseppe Cota, Riccardo Zese, Elena Bellodi, Evelina Lamma, and Fabrizio Riguzzi. Structure learning with distributed parameter learning for probabilistic ontologies. In Doctoral Consortium of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pages 75-84, © by the authors, 2015. [bib | http | http ]
Riguzzi, F., Bellodi, E., Lamma, E., Zese, R., Cota, G.: Learning Probabilistic Description Logics. In Uncertainty Reasoning for the Semantic Web III – ISWC International Workshops, URSW 2011-2013, Revised Selected Papers, volume 8816 of Lecture Notes in Computer Science, pages 63-78. Springer, 2013.