@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}
}
@article{RigCotBel17-IJAR-IJ,
author = {Fabrizio Riguzzi and Giuseppe Cota and
Elena Bellodi and Riccardo Zese },
title = {Causal Inference in {cplint}},
journal = {International Journal of Approximate Reasoning},
year = {2017},
publisher = {Elsevier},
address = {Amsterdam},
copyright = {Elsevier},
doi = {10.1016/j.ijar.2017.09.007},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/RigCotBel-IJAR17.pdf},
abstract = {
cplint is a suite of programs for reasoning and learning with Probabilistic Logic
Programming languages that follow the distribution semantics.
In this paper we describe how we have extended cplint to perform causal reasoning.
In particular, we consider Pearl's do calculus for models where all
the variables are measured.
The two cplint modules for inference, PITA and MCINTYRE, have been extended for
computing the effect of actions/interventions on these models.
We also executed experiments comparing exact and approximate inference with
conditional and causal queries, showing that causal inference is often cheaper than conditional inference.
},
keywords = {
Probabilistic Logic Programming, Distribution Semantics, Logic Programs with Annotated Disjunctions, ProbLog, Causal Inference, Statistical Relational Artificial Intelligence
},
volume = {91},
pages = {216-232},
month = {December},
number = {Supplement C},
issn = {0888-613X},
scopus = {2-s2.0-84992199737},
wos = {WOS:000391080100020}
}
@article{AlbBelCot17-IA-IJ,
author = {Marco Alberti and Elena Bellodi and Giuseppe Cota and
Fabrizio Riguzzi and Riccardo Zese},
title = {\texttt{cplint} on {SWISH}: Probabilistic Logical Inference with a Web Browser},
journal = {Intelligenza Artificiale},
publisher = {IOS Press},
copyright = {IOS Press},
year = {2017},
issn-print = {1724-8035},
issn-online = {2211-0097},
url = {http://ml.unife.it/wp-content/uploads/Papers/AlbBelCot-IA17.pdf},
abstract = {
\texttt{cplint} on SWISH is a web application that allows users to
perform reasoning tasks on probabilistic logic programs.
Both inference and learning systems can be performed: 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 and particle filtering are used.
\texttt{cplint} on SWISH is also able to sample goals' arguments and
to graph the results. This paper reports on advances and new features
of \texttt{cplint} on SWISH, including the capability of drawing the
binary decision diagrams created during the inference processes.
},
keywords = { Logic Programming, Probabilistic Logic Programming,
Distribution Semantics, Logic Programs with Annotated Disjunctions, Web
Applications
},
volume = {11},
number = {1},
doi = {10.3233/IA-170106},
pages = {47--64},
wos = {WOS:000399736500004}
}
@article{BelLamRig17-SPE-IJ,
author = {Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi and
Riccardo Zese and Giuseppe Cota},
title = {A web system for reasoning with probabilistic {OWL}},
journal = {Software: Practice and Experience},
publisher = {Wiley},
copyright = {Wiley},
year = {2017},
doi = {10.1002/spe.2410},
issn = {1097-024X},
month = {January},
pages = {125--142},
volume = {47},
number = {1},
scopus = {2-s2.0-84992412060},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelLamRig-SPE16.pdf},
abstract = {
We present the web application TRILL on SWISH, which allows the user to write probabilistic Description Logic (DL) theories and compute the probability of queries with just a web browser.
Various probabilistic extensions of DLs have been proposed in the recent past, since uncertainty is a fundamental component of the Semantic Web.
We consider probabilistic DL theories following our DISPONTE semantics. Axioms of a DISPONTE Knowledge Base (KB) can be annotated with a probability and the probability of queries can be computed with inference algorithms.
TRILL is a probabilistic reasoner for DISPONTE KBs that is implemented in Prolog and exploits its backtracking facilities for handling the non-determinism of the tableau algorithm.
TRILL on SWISH is based on SWISH, a recently proposed web framework for logic programming, based on various features and packages of SWI-Prolog (e.g., a web server and a library for creating remote Prolog engines and posing queries to them). TRILL on SWISH also allows users to cooperate in writing a probabilistic DL theory.
It is free, open, and accessible on the Web at the url: \trillurl; it includes a number of examples that cover a wide range of domains and provide interesting Probabilistic Semantic Web applications.
By building a web-based system, we allow users to experiment with Probabilistic DLs without the need to install a complex software stack. In this way we aim to reach out to a wider audience and popularize the Probabilistic Semantic Web.
},
keywords = { Semantic Web, Web Applications, Description Logics, Probabilistic Description Logics, SWI-Prolog, Logic Programming
}
}
@article{RigBelZes17-IJAR-IJ,
author = {Fabrizio Riguzzi and
Elena Bellodi and Riccardo Zese and
Giuseppe Cota and
Evelina Lamma },
title = {A Survey of Lifted Inference Approaches for Probabilistic
Logic Programming under the Distribution Semantics},
journal = {International Journal of Approximate Reasoning},
year = {2017},
publisher = {Elsevier},
address = {Amsterdam},
copyright = {Elsevier},
doi = {10.1016/j.ijar.2016.10.002},
url = {http://ml.unife.it/wp-content/uploads/Papers/RigBelZes-IJAR17.pdf},
volume = {80},
number = {Supplement C},
issn = {0888-613X},
pages = {313--333},
month = {January},
abstract = {
Lifted inference aims at answering queries from statistical relational models by reasoning on populations of individuals as a
whole instead of considering each individual singularly.
Since the initial proposal by David Poole in 2003, many lifted inference techniques have appeared, by lifting different algorithms or using approximation involving different kinds of models, including parfactor graphs and Markov Logic Networks.
Very recently lifted inference was applied to Probabilistic Logic Programming (PLP) under the distribution semantics, with proposals such as LP2 and Weighted First-Order Model Counting
(WFOMC). Moreover, techniques for dealing with aggregation parfactors can be directly applied to PLP.
In this paper we survey these approaches and present an
experimental comparison on five models.
The results show that WFOMC outperforms the other approaches, being able to exploit more symmetries.
},
keywords = {Probabilistic Logic Programming, Lifted Inference, Variable Elimination, Distribution Semantics, ProbLog, Statistical Relational Artificial Intelligence
},
scopus = {2-s2.0-84992199737},
wos = {WOS:000391080100020}
}
@article{ZesBelRig18-AMAI-IJ,
author = {Riccardo Zese and
Elena Bellodi and
Fabrizio Riguzzi and
Giuseppe Cota and
Evelina Lamma },
title = {Tableau Reasoning for Description Logics and its Extension to Probabilities},
journal = {Annals of Mathematics and Artificial Intelligence},
publisher = {Springer},
copyright = {Springer},
year = {2018},
issn-print = {1012-2443},
issn-online = {1573-7470},
url = {http://ml.unife.it/wp-content/uploads/Papers/ZesBelRig-AMAI16.pdf},
pdf = {http://rdcu.be/kONG},
month = {March},
day = {01},
volume = {82},
number = {1},
pages = {101--130},
doi = {10.1007/s10472-016-9529-3},
abstract = {
The increasing popularity of the Semantic Web drove to a wide-
spread adoption of Description Logics (DLs) for modeling real world domains.
To help the diffusion of DLs, a large number of reasoning algorithms have been
developed. Usually these algorithms are implemented in procedural languages
such as Java or C++. Most of the reasoners exploit the tableau algorithm
which features non-determinism, that is not easily handled by those languages.
Prolog directly manages non-determinism, thus is a good candidate for dealing
with the tableau's non-deterministic expansion rules.
We present TRILL, for "Tableau Reasoner for descrIption Logics in pro-
Log", that implements a tableau algorithm and is able to return explanations
for queries and their corresponding probability, and TRILLP , for "TRILL
powered by Pinpointing formulas", which is able to compute a Boolean for-
mula representing the set of explanations for a query. Reasoning on real world
domains also requires the capability of managing probabilistic and uncertain
information. We show how TRILL and TRILLP can be used to compute the
probability of queries to knowledge bases following DISPONTE semantics.
Experiments comparing these with other systems show the feasibility of the
approach.},
keywords = { Description Logics, Tableau, Prolog, Semantic Web},
scopus = {2-s2.0-84990986085}
}
@article{GavLam18-FI-IJ,
author = {Gavanelli, Marco and Lamma, Evelina and Riguzzi, Fabrizio and Bellodi, Elena and Zese, Riccardo and Cota, Giuseppe},
title = {Reasoning on Datalog+- Ontologies with Abductive Logic Programming},
year = {2018},
journal = {Fundamenta Informaticae},
copyright = {IOS Press},
volume = {159},
doi = {10.3233/FI-2018-1658},
pages = {65--93},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/GavLam-FI18.pdf},
scopus = {2-s2.0-85043572529}
}
@article{ZesBelCot19-TPLP-IJ,
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},
copyright = {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}
}
@article{CheCotGavLamMelRig20-EAAI-IJ,
author = {Federico Chesani and
Giuseppe Cota and
Marco Gavanelli and
Evelina Lamma and
Paola Mello and
Fabrizio Riguzzi},
title = {Declarative and Mathematical Programming approaches to Decision Support
Systems for food recycling},
journal = {Engineering Applications of Artificial Intelligence},
volume = {95},
pages = {103861},
year = {2020},
doi = {10.1016/j.engappai.2020.103861},
scopus = {2-s2.0-85089188550}
}
@article{LosVen21-JEGTP-IJ,
author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giuseppe Fabio and Bechini, Giovanni and Cota, Giuseppe and Riguzzi, Fabrizio},
title = {Structured Methodology for Clustering Gas Turbine Transients by means of Multi-variate Time Series},
year = {2021},
publisher = {ASME},
journal = {Journal of Engineering for Gas Turbines and Power},
volume = {143},
number = {3},
pages = {031014-1 (13 pages)},
doi = {10.1115/1.4049503}
}
@article{ZesCot21-JWS-IJ,
title = {Optimizing a tableau reasoner and its implementation in Prolog},
journal = {Journal of Web Semantics},
volume = {71},
number = {100677},
pages = {1--22},
year = {2021},
issn = {1570-8268},
doi = {https://doi.org/10.1016/j.websem.2021.100677},
url = {https://www.sciencedirect.com/science/article/pii/S1570826821000524},
author = {Riccardo Zese and Giuseppe Cota},
keywords = {Reasoner, Axiom pinpointing, Tableau algorithm, (Probabilistic) description logic, Prolog},
abstract = {One of the foremost reasoning services for knowledge bases is finding all the justifications for a query. This is useful for debugging purpose and for coping with uncertainty. Among Description Logics (DLs) reasoners, the tableau algorithm is one of the most used. However, in order to collect the justifications, the reasoners must manage the non-determinism of the tableau method. For these reasons, a Prolog implementation can facilitate the management of such non-determinism. The TRILL framework contains three probabilistic reasoners written in Prolog: TRILL, TRILLP and TORNADO. Since they are all part of the same framework, the choice about which to use can be done easily via the framework settings. Each one of them uses different approaches for probabilistic inference and handles different DLs flavors. Our previous work showed that they can sometimes achieve better results than state-of-the-art (non-)probabilistic reasoners. In this paper we present two optimizations that improve the performances of the TRILL reasoners. The first one consists into identifying the fragment of the KB that allows to perform inference without losing the completeness. The second one modifies which tableau rule to apply and their order of application, in order to reduce the number of operations. Experimental results show the effectiveness of the introduced optimizations.}
}
@article{LosVen22-JEGTP-IJ,
author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giuseppe Fabio and Bechini, Giovanni and Cota, Giuseppe and Riguzzi, Fabrizio},
title = {Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models},
journal = {Journal of Engineering for Gas Turbines and Power},
volume = {144},
number = {3},
year = {2022},
issn = {0742-4795},
doi = {10.1115/1.4053194},
publisher = asme_p,
note = {{GTP-21-1324}}
}
@article{Rig24-JCS-IJ,
article_type = {journal},
title = {Machine Learning Approaches for the Prediction of Gas Turbine Transients},
author = {Fadja, Arnaud Nguembang and Cota, Giuseppe and Bertasi, Francesco and Riguzzi, Fabrizio and Losi, Enzo and Manservigi, Lucrezia and Venturini, Mauro and Bechini, Giovanni},
volume = {20},
number = {5},
year = {2024},
month = {Feb},
pages = {495-510},
doi = {10.3844/jcssp.2024.495.510},
url = {https://thescipub.com/abstract/jcssp.2024.495.510},
abstract = {Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}
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