author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and
  Riccardo Zese and Giuseppe Cota},
  title = {Probabilistic Logic Programming on the Web},
  journal = {Software: Practice and Experience},
  publisher = {Wiley},
  copyright = {Wiley},
  year = {2016},
  issn = {1097-024X},
  url = {http://ml.unife.it/wp-content/uploads/Papers/RigBelLam-SPE16.pdf},
  abstract = {
We present the web application "cplint on SWISH", that allows the user
to write probabilistic logic programs and compute the probability of queries
with just a web browser. The application is based on SWISH, a recently
proposed web framework for logic programming. SWISH is based on various
features and packages of SWI-Prolog, in particular its web server and
its Pengine library, that allow to create remote Prolog engines and to pose
queries to them. In order to develop the web application, we started from
the PITA system which is included in cplint, a suite of programs for reasoning
on Logic Programs with Annotated Disjunctions, by porting PITA
to SWI-Prolog. Moreover, we modified the PITA library so that it can be
executed in a multi-threading environment. Developing "cplint on SWISH"
also required modification of the JavaScript SWISH code that creates and
queries Pengines. "cplint on SWISH" includes a number of examples that
cover a wide range of domains and provide interesting applications of Probabilistic
Logic Programming (PLP). By providing a web interface to cplint
we allow users to experiment with PLP without the need to install a system,
a procedure which is often complex, error prone and limited mainly to the
Linux platform. In this way, we aim to reach out to a wider audience and
popularize PLP.},
  keywords = { Logic Programming, Probabilistic Logic Programming,
Distribution Semantics, Logic Programs with Annotated Disjunctions, Web
  doi = {10.1002/spe.2386},
  volume = {46},
  number = {10},
  pages = {1381-1396},
  month = {October},
  wos = {WOS:000383624900005},
  scopus = {2-s2.0-84951829971}
  title = {Probabilistic Constraint Logic Theories},
  author = {Marco Alberti and Elena Bellodi and Giuseppe Cota and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese},
  pages = {15--28},
  url = {http://ceur-ws.org/Vol-1661/#paper-02},
  pdf = {http://ceur-ws.org/Vol-1661/paper-02.pdf},
  booktitle = {Proceedings of the 3nd International Workshop on Probabilistic Logic Programming ({PLP})},
  year = 2016,
  editor = {Arjen Hommersom and
Samer Abdallah},
  volume = 1661,
  series = {CEUR Workshop Proceedings},
  address = {Aachen, Germany},
  issn = {1613-0073},
  venue = {London, UK},
  eventdate = {2016-09-03},
  publisher = {Sun {SITE} Central Europe},
  copyright = {by the authors},
  abstract = {Probabilistic logic models are used ever more often to deal with
the uncertain relations typical of the real world.
However, these models usually require expensive inference procedures. Very recently the problem of identifying tractable
languages has come to the fore.
In this paper we consider the  models used by the learning from interpretations
ILP setting, namely
sets of integrity constraints, and propose a probabilistic version
of them. A semantics in the style of the distribution semantics is adopted, where each integrity constraint is annotated with a probability.
These probabilistic constraint logic models assign a probability of being positive to interpretations. This probability can be computed
in a time that is logarithmic in the
number of ground instantiations of violated constraints.
This formalism can be used as the target language in learning systems and
for declaratively specifying the behavior of a system.
In the latter case, inference corresponds to computing the probability of compliance
of a system's behavior to the model.
  keywords = {
Probabilistic Logic Programming, Distribution Semantics, Constraint Logic
  scopus = {2-s2.0-84987763948}
  year = {2016},
  booktitle = {22nd European Conference  on Artificial Intelligence {ECAI 2016}},
  venue = {The Hague, Netherlands},
  eventdate = {August 29-September 2, 2016},
  editor = {Maria Fox and Gal Kaminka},
  title = {Scaling Structure Learning of Probabilistic Logic Programs by MapReduce},
  author = {Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota and Evelina Lamma},
  abstract = {Probabilistic Logic Programming is a promising formalism for dealing with uncertainty. Learning probabilistic logic programs has been receiving an increasing attention in Inductive Logic Programming: for instance,
the system SLIPCOVER learns high quality theories in a variety of domains. However, SLIPCOVER is computationally expensive, with a running time of the order of hours.
In order to apply SLIPCOVER to Big Data, we present SEMPRE, for ``Structure lEarning by MaPREduce", that scales SLIPCOVER by following a MapReduce strategy, directly implemented with the Message Passing Interface. },
  keywords = {Probabilistic Logic Programming, Parameter Learning, Structure Learning, MapReduce},
  series = {Frontiers in Artificial Intelligence and Applications},
  volume = {285},
  pages = {1602-1603},
  url = {http://ebooks.iospress.nl/volumearticle/44940},
  doi = {10.3233/978-1-61499-672-9-1602},
  wos = {WOS:000385793700205},
  scopus = {2-s2.0-85013029084},
  copyright = {CC-BY-NC 4.0},
  publisher = {IOS Press}
  booktitle = {Inductive Logic Programming: 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers},
  editor = {Katsumi Inoue and Hayato Ohwada and Akihiro Yamamoto},
  title = {Distributed Parameter Learning for Probabilistic Ontologies},
  author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
  pdf = {http://ml.unife.it/wp-content/uploads/Papers/CotZesBel-ILP15.pdf},
  year = {2016},
  publisher = {Springer International Publishing},
  address = {Heidelberg, Germany},
  series = {Lecture Notes in Computer Science},
  volume = {9575},
  copyright = {Springer International Publishing Switzerland},
  venue = {Kyoto, Japan},
  eventdate = {August 20-22, 2015},
  pages = {30--45},
  isbn-online = {978-3-319-40566-7},
  isbn-print = {978-3-319-40565-0},
  issn = {0302-9743},
  doi = {10.1007/978-3-319-40566-7_3},
  abstract = {Representing uncertainty in Description Logics has recently
received an increasing attention because of its potential to model real
world domains. EDGE for Em over bDds for description loGics param-
Eter learning is an algorithm for learning the parameters of probabilistic
ontologies from data. However, the computational cost of this algorithm
is significant since it may take hours to complete an execution. In this
paper we present EDGEMR, a distributed version of EDGE that exploits
the MapReduce strategy by means of the Message Passing Interface. Ex-
periments on various domains show that EDGEMR signicantly reduces
EDGE running time.},
  keywords = {Probabilistic Description Logics, Parameter Learning, MapReduce,
Message Passing Interface},
  note = {The final publication is available at Springer via
  author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
  title = {Statistical Relational Learning for Workflow Mining},
  journal = {Intelligent Data Analysis},
  publisher = {IOS Press},
  copyright = {IOS Press},
  year = {2016},
  doi = {10.3233/IDA-160818},
  month = {April},
  volume = {20},
  number = {3},
  pages = {515-541},
  url = {http://ml.unife.it/wp-content/uploads/Papers/BelRigLam-IDA15.pdf},
  keywords = {Workflow Mining, Process Mining, Knowledge-based Process Models, Inductive Logic Programming, Statistical Relational Learning,
Business Process Management
  abstract = {
The management of business processes can support  efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language.
Various recently proposed knowledge-based languages showed advantages over graph-based procedural notations.
Moreover, rapid changes of the environment require organizations to check how compliant are new process instances with the deployed models.
We present a Statistical Relational Learning approach to Workflow Mining that takes into account both flexibility and uncertainty in real environments.
It performs  automatic discovery of  process models expressed in a probabilistic logic.
It  uses the existing DPML  algorithm  for extracting  first-order logic constraints from process logs. The constraints are then translated into Markov Logic to learn their weights.
Inference on the resulting Markov Logic  model allows a probabilistic classification of test traces, by assigning them the probability of being compliant to the model.
We applied this approach to three datasets and compared it with DPML alone, five Petri net- and EPC-based process mining algorithms and Tilde.
The technique is able to better classify new execution traces, showing higher  accuracy and areas under the PR/ROC curves in most cases.
  scopus = {2-s2.0-84969808336},
  wos = {WOS:000375005000004}

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