@article{RigBelLam16-SPE-IJ,
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
Applications
},
doi = {10.1002/spe.2386},
volume = {46},
number = {10},
pages = {1381-1396},
month = {October},
wos = {WOS:000383624900005},
scopus = {2-s2.0-84951829971}
}
@inproceedings{Rig16-PLP-IW,
title = {Deductive and Inductive Probabilistic Programming (invited talk)},
author = {Fabrizio Riguzzi},
pages = {1--1},
url = {http://ceur-ws.org/Vol-1661/#invited-01},
pdf = {http://ceur-ws.org/Vol-1661/invited-01.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 programming (PP) is available in two different
variants: imperative/functional and logic. These two variants have complementary
strengths and mostly separate communities. In this talk I
will discuss how most strengths of inference for imperative/functional
PP can be included in PLP. Moreover, I will show that PLP is particularly
suitable for inductive reasoning.
},
keywords = {
Probabilistic Logic Programming, Probabilistic Programming, Distribution Semantics}
}
@inproceedings{AlbBelCot16-PLP-IW,
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
Theories},
scopus = {2-s2.0-84987763948}
}
@inproceedings{RigBelZes16-ECAI-IC,
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}
}
@inproceedings{AlbCotRigZes16-AIIA-IC,
booktitle = {Proceedings of the 15th Conference of the Italian Association for Artificial Intelligence ({AI*IA2016}),
Genova, Italy, 28 November - 1 December 2016},
editor = {Giovanni Adorni and Stefano Cagnoni and Marco Gori and Marco Maratea},
year = {2016},
title = {Probabilistic Logical Inference On the Web},
author = {Marco Alberti and Giuseppe Cota and Fabrizio Riguzzi and Riccardo Zese},
abstract = {cplint on SWISH is a web application for probabilistic
logic programming. It allows users to perform inference and
learning using just a web browser, with the computation performed
on the server. In this paper we report on recent advances in the
system, namely the inclusion of algorithms for computing
conditional probabilities with exact, rejection sampling and
Metropolis-Hasting methods. Moreover, the system now allows hybrid
programs, i.e., programs where some of the random variables are
continuous. To perform inference on such programs likelihood
weighting is used that makes it possible to also have evidence on
continuous variables. cplint on SWISH offers also the
possibility of sampling arguments of goals, a kind of inference
rarely considered but useful especially when the arguments are
continuous variables. Finally, cplint on SWISH offers the
possibility of graphing the results, for example by drawing the
distribution of the sampled continuous arguments of goals.},
publisher = {Springer International Publishing},
address = {Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = {10037},
copyright = {Springer International Publishing AG},
keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Hybrid program},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/AlbCotRig-AIXIA16.pdf},
doi = {10.1007/978-3-319-49130-1_26},
pages = {351-363},
venue = {Genova, Italy},
eventdate = {November 28-December 1, 2016},
isbn-online = {978-3-319-49129-5},
isbn-print = {978-3-319-49130-1},
issn = {0302-9743},
scopus = {2-s2.0-85006074125},
wos = {WOS:000389797400026},
note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-49130-1_26}}
}
@inproceedings{AlbLamRigZes16-AIIA-IC,
booktitle = {Proceedings of the 15th Conference of the Italian Association for Artificial Intelligence ({AI*IA2016}),
Genova, Italy, 28 November - 1 December 2016},
editor = {Giovanni Adorni and Stefano Cagnoni and Marco Gori and Marco Maratea},
year = {2016},
title = {Probabilistic Hybrid Knowledge Bases under the Distribution
Semantics},
author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese},
abstract = {Since Logic Programming (LP) and Description Logics (DLs) are based on
different assumptions (the closed and the open world assumption,
respectively), combining them provides higher expressiveness in
applications that require both
assumptions.
Several proposals have been made to combine LP and DLs. An especially
successful line of research is the one based on the Lifschitz's
logic of Minimal Knowledge with Negation as Failure (MKNF). Motik
and Rosati introduced Hybrid knowledge bases (KBs), composed of LP
rules and DL axioms, gave them an MKNF semantics and
studied their complexity. Knorr et al. proposed a well-founded semantics for
Hybrid KBs where the LP clause heads are non-disjunctive, which
keeps querying polynomial (provided the underlying DL is polynomial)
even when the LP portion is non-stratified.
In this paper, we propose Probabilistic Hybrid Knowledge Bases (PHKBs),
where the atom in the head of LP clauses and each DL axiom is
annotated with a probability value. PHKBs are given a distribution
semantics by defining a probability distribution over deterministic
Hybrid KBs. The probability of a query being true is the sum of the
probabilities of the deterministic KBs that entail the query. Both
epistemic and statistical probability can be addressed, thanks to
the integration of probabilistic LP and DLs.},
publisher = {Springer International Publishing},
address = {Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = {10037},
copyright = {Springer International Publishing AG},
issn = {0302-9743},
keywords = {Probabilistic Logic Programming, Probabilistic Description Logics, Hybrid Knowledge Bases},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/AlbLamRig-AIXIA16.pdf},
doi = {10.1007/978-3-319-49130-1_27},
pages = {364-376},
venue = {Genova, Italy},
scopus = {2-s2.0-85005950065},
wos = {WOS:000389797400027},
eventdate = {November 28-December 1, 2016},
isbn-online = {978-3-319-49129-5},
isbn-print = {978-3-319-49130-1},
note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-49130-1_27}}
}
@inproceedings{CotZesBel16-ILP-IC,
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
\url{http://dx.doi.org/10.1007/978-3-319-40566-7_3}}
}
@article{Rig16-IJAR-IJ,
author = {Fabrizio Riguzzi},
title = {The Distribution Semantics for Normal Programs with Function Symbols},
journal = {International Journal of Approximate Reasoning},
year = {2016},
publisher = {Elsevier},
address = {Amsterdam},
doi = {10.1016/j.ijar.2016.05.005},
volume = {77},
number = {Supplement C},
pages = {1 - 19},
issn = {0888-613X},
month = {October},
url = {http://ml.unife.it/wp-content/uploads/Papers/Rig-IJAR16.pdf},
copyright = {Elsevier},
abstract = {The distribution semantics integrates logic programming and probability theory using a possible worlds approach.
Its intuitiveness and simplicity has made it the most
widely used semantics for probabilistic logic programming,
with successful applications in many domains.
When the program has function symbols, the semantics was defined for special cases: either the program has to be definite or the queries must have a finite number of finite explanations.
In this paper we show that it is possible to define the semantics for all programs. We also show that this definition coincides with that of Sato and Kameya on positive programs.
Moreover, we highlight possible approaches for inference, both exact and
approximate.
},
keywords = {Distribution Semantics, Function Symbols,
ProbLog,
Probabilistic Logic Programming
},
wos = {WOS:000381164500001},
scopus = {2-s2.0-84973659532}
}
@article{RigCot16-ALP-INVJ,
author = {Fabrizio Riguzzi and Giuseppe Cota},
title = {Probabilistic Logic Programming Tutorial},
journal = {The Association for Logic Programming Newsletter},
year = {2016},
volume = {29},
number = {1},
month = {March/April},
publisher = {The Association for Logic Programming},
address = {\London, \UK},
copyright = {by the authors},
url = {http://www.cs.nmsu.edu/ALP/2016/03/probabilistic-logic-programming-tutorial/},
abstract = {Probabilistic Logic Programming (PLP) introduces probabilistic reasoning in Logic Programs in order to represent uncertain information. It is receiving an increased attention due to its applications in particular in the Machine Learning field.
In this tutorial we will show how to use cplint on SWISH, a web application for performing inference and learning on user-defined probabilistic logic programs. You will learn how to write a probabilistic logic program processable by cplint on SWISH, how to execute the different types of queries allowed by this application and how to perform learning.
cplint on SWISH is based on SWISH, a web framework for Logic Programming, and on cplint , a suite of programs for inference and learning of Logic Programs with annotated disjunctions (LPADs) . It keeps the same syntax of cplint and as cplint it uses Logic Programs with annotated disjunctions (LPADs) as formalism to represent probabilistic logic programs.},
keywords = {Probabilistic Logic Programming, Distribution Semantics, Probabilistic Inductive
Logic Programming, Machine Learning
}
}
@article{BelRigLam16-IDA-IJ,
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}
}
@article{Rig16-MNg-RE,
author = {Fabrizio Riguzzi},
title = {Review of
{Echenim, M.; Peltier, N. A superposition calculus for abductive reasoning. J. Automat. Reason. 57, no. 2, 97--134, 2016.
}},
journal = {Mathematical Reviews},
publisher = {American Mathematical Society},
copyright = {American Mathematical Society},
year = {2016},
month = {December},
issn = {2167-5163},
mrnumber = {MR3518670},
mrreviewer = {Fabrizio Riguzzi},
url = {http://www.ams.org/mathscinet-getitem?mr=3518670 }
}
@article{Rig16-MNf-RE,
author = {Fabrizio Riguzzi},
title = {Review of
{Kaminski, Benjamin Lucien; Katoen, Joost-Pieter; Matheja, Christoph; Olmedo, Federico Weakest precondition reasoning for expected run-times of probabilistic programs. Programming languages and systems, 364--389, Lecture Notes in Comput. Sci., 9632, Springer, Berlin, 2016.}},
journal = {Mathematical Reviews},
publisher = {American Mathematical Society},
copyright = {American Mathematical Society},
year = {2016},
month = {October},
issn = {2167-5163},
mrnumber = {MR3516285},
mrreviewer = {Fabrizio Riguzzi},
url = {http://www.ams.org/mathscinet-getitem?mr=3516285 }
}
@article{Rig16-MNe-RE,
author = {Fabrizio Riguzzi},
title = {Review of
{Drabent, W\l{}odzimierz On definite program answers and least Herbrand models. Theory Pract. Log. Program. 16 (2016), no. 4, 498--508.}},
journal = {Mathematical Reviews},
publisher = {American Mathematical Society},
copyright = {American Mathematical Society},
year = {2016},
month = {October},
issn = {2167-5163},
mrnumber = {MR3506921},
mrreviewer = {Fabrizio Riguzzi},
url = {http://www.ams.org/mathscinet-getitem?mr=3506921 }
}
@article{Rig16-MNd-RE,
author = {Fabrizio Riguzzi},
title = {Review of
{Jansen, Nils; Kaminski, Benjamin Lucien; Katoen, Joost-Pieter; Olmedo, Federico; Gretz, Friedrich; McIver, Annabelle Conditioning in probabilistic programming. The 31st Conference on the Mathematical Foundations of Programming Semantics (MFPS XXXI), 199--216, Electron. Notes Theor. Comput. Sci., 319, Elsevier Sci. B. V., Amsterdam, 2015. }},
journal = {Mathematical Reviews},
publisher = {American Mathematical Society},
copyright = {American Mathematical Society},
year = {2016},
month = {July},
issn = {2167-5163},
mrnumber = {3472405},
mrreviewer = {Fabrizio Riguzzi},
url = {http://www.ams.org/mathscinet-getitem?mr=3472405 }
}
@article{Rig16-MNc-RE,
author = {Fabrizio Riguzzi},
title = {Review of
{Alviano, Mario; Faber, Wolfgang
Supportedly stable answer sets for logic programs with generalized atoms. (English summary) Web reasoning and rule systems, 30-44,
Lecture Notes in Comput. Sci., 9209, Springer, 2015. }},
journal = {Mathematical Reviews},
publisher = {American Mathematical Society},
copyright = {American Mathematical Society},
year = {2016},
month = {April},
issn = {2167-5163},
mrnumber = {3441721},
mrreviewer = {Fabrizio Riguzzi},
url = {http://www.ams.org/mathscinet-getitem?mr=3441721}
}
@article{Rig16-MNb-RE,
author = {Fabrizio Riguzzi},
title = {Review of
{Chowdhury, Md. Solimul; Liu, Fangfang; Chen, Wu; Karimi, Arash; You, Jia-Huai
Polynomial approximation to well-founded semantics for logic programs with generalized atoms: case studies. Logic-based program synthesis and transformation, 279-296,
Lecture Notes in Comput. Sci., 8981, Springer, 2015.}},
journal = {Mathematical Reviews},
publisher = {American Mathematical Society},
copyright = {American Mathematical Society},
year = {2016},
month = {January},
issn = {2167-5163},
mrnumber = {3368319},
mrreviewer = {Fabrizio Riguzzi},
url = {http://www.ams.org/mathscinet-getitem?mr=3368319}
}
@article{Rig16-MNa-RE,
author = {Fabrizio Riguzzi},
title = {Review of
{Janhunen, Tomi; Niemel\"a, Ilkka Cumulativity tailored for nonmonotonic reasoning. Advances in knowledge representation, logic programming, and abstract argumentation, 96--111, Lecture Notes in Comput. Sci., 9060, Springer, 2015. }},
journal = {Mathematical Reviews},
publisher = {American Mathematical Society},
copyright = {American Mathematical Society},
year = {2016},
month = {January},
issn = {2167-5163},
mrnumber = {3368675},
mrreviewer = {Fabrizio Riguzzi},
url = {http://www.ams.org/mathscinet-getitem?mr=3368675}
}
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