bookchapters.bib

@incollection{RigBel14-URSWa-BC,
  year = {2014},
  isbn = {978-3-319-13412-3},
  booktitle = {Uncertainty Reasoning for the Semantic Web III},
  series = {Lecture Notes in Computer Science},
  editor = {Bobillo, Fernando and Carvalho, Rommel N. and Costa, Paulo C.G. and d'Amato, Claudia and Fanizzi, Nicola and Laskey, Kathryn B. and Laskey, Kenneth J. and Lukasiewicz, Thomas and Nickles, Matthias and Pool, Michael},
  doi = {10.1007/978-3-319-13413-0_4},
  title = {Learning Probabilistic Description Logics},
  publisher = {Springer International Publishing},
  copyright = {Springer International Publishing},
  author = {Riguzzi, Fabrizio and Bellodi, Elena and Lamma, Evelina and Zese, Riccardo and Cota, Giuseppe},
  pages = {63-78},
  pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigBel14-URSWa-BC.pdf},
  language = {English},
  volume = {8816},
  note = {The original publication is available at
\url{http://link.springer.com}}
}

@incollection{RigBel14-URSWb-BC,
  year = {2014},
  isbn = {978-3-319-13412-3},
  booktitle = {Uncertainty Reasoning for the Semantic Web III},
  series = {Lecture Notes in Computer Science},
  editor = {Bobillo, Fernando and Carvalho, Rommel N. and Costa, Paulo C.G. and d'Amato, Claudia and Fanizzi, Nicola and Laskey, Kathryn B. and Laskey, Kenneth J. and Lukasiewicz, Thomas and Nickles, Matthias and Pool, Michael},
  doi = {10.1007/978-3-319-13413-0_5},
  title = {Semantics and Inference for Probabilistic Description Logics},
  publisher = {Springer International Publishing},
  copyright = {Springer International Publishing},
  author = {Zese, Riccardo and Bellodi, Elena and Lamma, Evelina and Riguzzi, Fabrizio and Aguiari, Fabiano},
  pages = {79-99},
  language = {English},
  volume = {8816},
  url = {http://ml.unife.it/wp-content/uploads/Papers/RigBel14-URSWb-BC.pdf},
  note = {The original publication is available at
\url{http://link.springer.com}}
}

@incollection{CotZesBelLamRig20-SSWS-BC,
  title = {A Framework for Reasoning on Probabilistic Description Logics},
  author = {Cota, Giuseppe and Zese, Riccardo and Bellodi, Elena and Lamma, Evelina and Riguzzi, Fabrizio},
  booktitle = {Applications and Practices in Ontology Design, Extraction, and Reasoning},
  series = {Studies on the Semantic Web},
  volume = {49},
  editor = {Cota, Giuseppe and Daquino, Marilena and Pozzato, Gian Luca},
  isbn = {978-1-64368-142-9},
  doi = {10.3233/SSW200040},
  language = {English},
  pages = {127-144},
  year = {2020},
  publisher = {{IOS} Press},
  abstract = {While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics.
	In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics.
	The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.
	},
  copyright = {Akademische Verlagsgesellschaft AKA GmbH, Berlin}
}

@incollection{ZesBelFraRigLam22-MLNVM-BC,
  author = {Zese, Riccardo and Bellodi, Elena and Fraccaroli, Michele and Riguzzi, Fabrizio and Lamma, Evelina},
  editor = {Micheloni, Rino and Zambelli, Cristian},
  title = {Neural Networks and Deep Learning Fundamentals},
  booktitle = {Machine Learning and Non-volatile Memories},
  year = {2022},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {23--42},
  abstract = {In the last decade, Neural Networks (NNs) have come to the fore as one of the most powerful and versatile approaches to many machine learning tasks. Deep Learning (DL)Deep Learning (DL), the latest incarnation of NNs, is nowadays applied in every scenario that needs models able to predict or classify data. From computer vision to speech-to-text, DLDeep Learning (DL) techniques are able to achieve super-human performance in many cases. This chapter is devoted to give a (not comprehensive) introduction to the field, describing the main branches and model architectures, in order to try to give a roadmap of this area to the reader.},
  isbn = {978-3-031-03841-9},
  doi = {10.1007/978-3-031-03841-9_2},
  url = {https://doi.org/10.1007/978-3-031-03841-9_2}
}

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