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.
The last version of LEAP is available in the DL-Learner framework. The code can be downloaded from GitHub here.
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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 ]
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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 ]
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