title = {A Comparison of {MCMC} Sampling for Probabilistic Logic Programming},
  author = {Damiano Azzolini and Fabrizio Riguzzi and Evelina Lamma and Franco Masotti },
  booktitle = {Proceedings of the 18th Conference of the Italian Association for Artificial Intelligence ({AI*IA2019}),
Rende, Italy 19-22 November 2019},
  year = 2019,
  editor = {Mario Alviano and Gianluigi Greco and
Francesco Scarcello},
  publisher = {Springer},
  address = {Heidelberg, Germany},
  series = {Lecture Notes in Computer Science},
  venue = {Remnde, Italy},
  eventdate = {November 19-22, 2019},
  copyright = {Springer}
  author = {Riguzzi, Fabrizio and Kersting, Kristian and Lippi, Marco and Natarajan, Sriraam},
  title = {Editorial: Statistical Relational Artificial Intelligence},
  journal = {Frontiers in Robotics and AI},
  volume = {6},
  pages = {68},
  year = {2019},
  url = {https://www.frontiersin.org/article/10.3389/frobt.2019.00068},
  doi = {10.3389/frobt.2019.00068},
  issn = {2296-9144},
  copyright = {by the authors},
  publisher = {Frontiers Media SA},
  address = {Lausanne, 
  author = {Cota, Giuseppe and Riguzzi, Fabrizio and Lamma, Evelina and Zese, Riccardo},
  title = {{KRaider}: a Crawler for Linked Data},
  year = {2019},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  address = {Aachen, Germany},
  volume = {2396},
  editor = {Alberto Casagrande and Eugenio Omodeo},
  booktitle = {Proceedings of the 34th Italian Conference on Computational Logic},
  url = {http://ceur-ws.org/Vol-2396/paper35.pdf},
  pages = {202-216},
  eventdate = {June 19-21, 2019},
  venue = {Trieste, Italy},
  issn = {1613-0073},
  copyright = {by the authors}
  author = {Nguembang Fadja, Arnaud  and Fabrizio Riguzzi},
  title = {Lifted Discriminative Learning of Probabilistic Logic Programs},
  journal = {Machine Learning},
  publisher = {Springer},
  copyright = {Springer},
  year = {2019},
  url = {http://ml.unife.it/wp-content/uploads/Papers/NguRig-ML18.pdf},
  doi = {10.1007/s10994-018-5750-0},
  abstract = {
Probabilistic logic programming (PLP) provides a powerful tool for reason- ing with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorithm for performing parameter and structure learning of these models from positive and negative exam- ples. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER. The results of the comparison of LIFTCOVER with SLIPCOVER on 12 datasets show that it can achieve solutions of similar or better quality in a fraction of the time.
  keywords = { Statistical Relational Learning, Probabilistic Inductive Logic Program- ming, Probabilistic Logic Programming, Lifted Inference, Expectation Maximization
  scopus = {2-s2.0-85052570852},
  volume = {108},
  number = {7},
  pages = {1111--1135}
  author = {Lachiche, Nicolas and Vrain, Christel and Riguzzi, Fabrizio and Bellodi Elena and Zese Riccardo},
  title = {Preface to special issue on {Inductive Logic Programming}, {ILP} 2017 and 2018},
  journal = {Machine Learning},
  year = {2019},
  pages = {1-3},
  copyrigth = {Springer},
  publisher = {Springer US},
  doi = {10.1007/s10994-019-05790-6},
  scopus = {2-s2.0-85061585564}
  title = {Probabilistic {DL} Reasoning with Pinpointing Formulas: A Prolog-based Approach},
  doi = {10.1017/S1471068418000480},
  journal = {Theory and Practice of Logic Programming},
  publisher = {Cambridge University Press},
  author = {Zese, Riccardo and Cota, Giuseppe and Lamma, Evelina and Bellodi, Elena and Riguzzi, Fabrizio},
  pages = {449--476},
  year = {2019},
  volume = {19},
  number = {3},
  pdf = {https://arxiv.org/pdf/1809.06180.pdf},
  scopus = {2-s2.0-85060024345},
  doi = {10.1017/S1471068418000480}
  author = {
Jan Wielemaker and Fabrizio Riguzzi and Bob Kowalski and Torbj\"orn Lager and Fariba Sadri and Miguel Calejo },
  title = {Using {SWISH} to realise interactive web based tutorials for logic based languages },
  journal = {Theory and Practice of Logic Programming},
  year = {2019},
  volume = {19},
  doi = {10.1017/S1471068418000522},
  number = {2},
  publisher = {Cambridge University Press},
  pages = {229-261},
  pdf = {https://arxiv.org/pdf/1808.08042.pdf},
  abstrac = {Programming environments have evolved from purely text based to using graphical user interfaces, 
  and now we see a move toward web-based interfaces, such as Jupyter. Web-based interfaces allow for the 
  creation of interactive documents that consist of text and programs, as well as their output. The output 
  can be rendered using web technology as, for example, text, tables, charts, or graphs. This approach 
  is particularly suitable for capturing data analysis workflows and creating interactive educational 
  material. This article describes SWISH, a web front-end for Prolog that consists of a web server 
  implemented in SWI-Prolog and a client web application written in JavaScript. SWISH provides a 
  web server where multiple users can manipulate and run the same material, and it can be adapted 
  to support Prolog extensions. In this article we describe the architecture of SWISH, and describe 
  two case studies of extensions of Prolog, namely Probabilistic Logic Programming and Logic Production 
  System, which have used SWISH to provide tutorial sites.},
  keywords = {Prolog, logic programming system, notebook interface, web},
  scopus = {2-s2.0-85061599946}

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