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  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 = {}
  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 = {},
  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
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
  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 = {},
  pdf = {},
  copyright = {Springer},
  scopus = {2-s2.0-74049114164}
  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
  editor = {Bernadette Bouchon-Meunier and Giulianella Coletti and Ronald
  publisher = {Elsevier},
  address = {Amsterdam, \TheNetherlands},
  isbn = {0-444-52075-9},
  year = {2006},
  pages = {207--217},
  doi = {10.1016/B978-044452075-3/50018-2},
  pdf = {},
  url = {},
  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
  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 = {},
  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}

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