@comment{{This file has been generated by bib2bib 1.96}}

@comment{{Command line: bib2bib -oc bookcahpters.cit -ob bookchapters.bib -c $key:"BC$" 2018.bib 2017.bib 2010.bib 2009.bib 2008.bib 2007.bib 2006.bib 2005.bib 2004.bib 2003.bib 2002.bib 2001.bib 2000.bib 1999.bib 1998.bib 1997.bib 1996.bib}}

@incollection{RigSwi18-WFS-BC, title = {Probabilistic Logic Programming Under the Distribution Semantics}, author = {Fabrizio Riguzzi and Theresa Swift}, year = {2018}, editor = {Michael Kifer and Yanhong A. Liu}, booktitle = {Declarative Logic Programming: Theory, Systems, and Applications}, publisher = {Association for Computing Machinery and Morgan \& Claypool}, pdf = {http://mcs.unife.it/~friguzzi/Papers/RigSwi-WFS16.pdf} }

@incollection{NguRig17-IMAKE-BC, author = {Arnaud {Nguembang Fadja} and Fabrizio Riguzzi}, title = {Probabilistic Logic Programming in Action}, booktitle = {Towards Integrative Machine Learning and Knowledge Extraction: BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers}, year = {2017}, editor = {Andreas Holzinger and Randy Goebel and Massimo Ferri and Vasile Palade}, publisher = {Springer}, address = {Heidelberg, \Germany}, series = {Lecture Notes in Computer Science}, volume = {10344}, copyright = {Springer}, doi = {10.1007/978-3-319-69775-8_5}, pdf = {http://mcs.unife.it/~friguzzi/Papers/NguRig17-IMAKE-BC.pdf}, abstract = {Probabilistic Programming (PP) has recently emerged as an effective approach for building complex probabilistic models. Until recently PP was mostly focused on functional programming while now Probabilistic Logic Programming (PLP) forms a significant subfield. In this paper we aim at presenting a quick overview of the features of current languages and systems for PLP. We first present the basic semantics for probabilistic logic programs and then consider extensions for dealing with infinite structures and continuous random variables. To show the modeling features of PLP in action, we present several examples: a simple generator of random 2D tile maps, an encoding of Markov Logic Networks, the truel game, the coupon collector problem, the one-dimensional random walk, latent Dirichlet allocation and the Indian GPA problem. These examples show the maturity of PLP. }, pages = {89--116}, keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Hybrid programs}, scopus = {2-s2.0-85033590324}, issn = {1860-949X}, isbn-print = {978-3-319-69774-1}, isbn-online = {978-3-319-69775-8}, note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-319-69775-8_5}} }

@incollection{CatLamRigSto10-ISKM-BC, author = {Massimiliano Cattafi and Evelina Lamma and Fabrizio Riguzzi and Sergio Storari}, title = {Incremental Declarative Process Mining}, booktitle = {Smart Information and Knowledge Management: Advances, Challenges, and Critical Issues}, year = {2010}, editor = {Ngoc Thanh Nguyen and Edward Szczerbicki}, publisher = {Springer}, address = {Heidelberg, \Germany}, series = {Studies in Computational Intelligence}, issn = {1860-949X}, isbn = {978-3-642-04583-7}, doi = {10.1007/978-3-642-04584-4_5}, volume = {260}, pages = {103--127}, abstract = {Business organizations achieve their mission by performing a number of processes. These span from simple sequences of actions to complex structured sets of activities with complex interrelation among them. The field of Business Processes Management studies how to describe, analyze, preserve and improve processes. In particular the subfield of Process Mining aims at inferring a model of the processes from logs (i.e. the collected records of performed activities). Moreover, processes can change over time to reflect mutated conditions, therefore it is often necessary to update the model. We call this activity Incremental Process Mining. To solve this problem, we modify the process mining system DPML to obtain IPM (Incremental Process Miner), which employs a subset of the SCIFF language to represent models and adopts techniques developed in Inductive Logic Programming to perform theory revision. The experimental results show that is more convenient to revise a theory rather than learning a new one from scratch. }, keywords = {Business Processes, Process Mining, Theory Revision}, url = {http://springerlink.com/content/663tx3001671j503/?p=1581a28611ac48088b750995a9767d5f&pi=4}, pdf = {http://mcs.unife.it/~friguzzi/Papers/CatLamRigSto09-ISKM-BC.pdf}, copyright = {Springer}, scopus = {2-s2.0-74049114164} }

@incollection{LamRigSto06-BC, author = {Evelina Lamma AND Fabrizio Riguzzi AND Sergio Storari}, title = {Improving the K2 Algorithm Using Association Rule Parameters}, booktitle = {Modern Information Processing: From Theory to Applications}, editor = {Bernadette Bouchon-Meunier and Giulianella Coletti and Ronald Yager}, publisher = {Elsevier}, address = {Amsterdam, \TheNetherlands}, isbn = {0-444-52075-9}, year = {2006}, pages = {207--217}, doi = {10.1016/B978-044452075-3/50018-2}, pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigSto-IPMUBK06.pdf}, url = {http://www.sciencedirect.com/science/article/pii/B9780444520753500182}, abstract = { A Bayesian network is an appropriate tool to work with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables and can be automatically elicited from database by Bayesian network learning algorithms such as K2. In the data mining field, association rules can also be interpreted as expressing statistical dependence relations. In this paper we present an extension of K2 called K2-rules that exploits a parameter normally defined in relation to association rules for learning Bayesian networks. We compare K2-rules with K2 and TPDA on the problems of learning four Bayesian networks. The experiments show that K2-rules improves both K2 and TPDA with respect to the quality of the learned network and K2 with respect to the execution time}, keywords = { Bayesian Networks, Machine Learning, Association Rules} }

@incollection{EspFerLam00-BC, author = {Floriana Esposito AND Stefano Ferilli AND Evelina Lamma AND Paola Mello AND Michela Milano AND Fabrizio Riguzzi AND Giovanni Semeraro}, title = {Cooperation of Abduction and Induction in Logic Programming}, booktitle = {Abductive and Inductive Reasoning: Essays on thier Relation and Integration}, editor = {Peter A. Flach and Antonis C. Kakas}, publisher = {Kluwer Academic Publishers}, address = {Dordrecht, \TheNetherlands}, year = {2000}, abstract = {We propose an integration of abduction and induction where the two inference processes cooperate in order to perform more powerful inferences. We assume the definitions of abduction and induction as given in Abductive Logic Programming and Inductive Logic Programming. Abduction helps induction by generating atomic hypotheses that can be used as new examples or for completing an incomplete background knowledge. Induction helps abduction by generalizing explanations. We present a learning algorithm that integrates abduction and induction. The algorithm solves a new learning problem where both the background and the target theory are abductive theories and abductive derivability is used as the example coverage relation. We then show how the algorithm can be applied to learning from incomplete knowledge and learning exceptions.}, keywords = {Abduction, Negation, Integrity_Constraints}, month = apr, pages = {233--252}, http = {http://www.springer.com/gp/book/9780792362500}, pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelRig-ABDINDBK00.pdf}, copyright = {Kluwer Academic Publishers}, ebook-isbn = {978-94-017-0606-3}, doi = {10.1007/978-94-017-0606-3}, hardcover-isbn = {978-0-7923-6250-0}, softcover-isbn = {978-90-481-5433-3}, series = {Applied Logic Series}, issn = {1386-2790} }

*This file was generated by
bibtex2html 1.96.*