workshops.bib

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@comment{{Command line: bin/bib2bib -ob temp/2012.bib -c 'author:"Zese" or (editor:"Zese" and $key:"EB$")' bib_files/2012.bib}}
@inproceedings{RigBelLamZes12-URSW12-IW,
  author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
  title = {Epistemic and Statistical Probabilistic Ontologies},
  booktitle = {Proceedings of the 8th International Workshop on Uncertain Reasoning for the Semantic Web (URSW2012), Boston, USA, 11 November 2012},
  year = {2012},
  editor = {Fernando Bobillo and
Rommel Carvalho and
da Costa, Paulo C. G. and
Nicola Fanizzi and
Laskey, Kathryn B.  and
Laskey, Kenneth J.  and
Thomas Lukasiewicz and
Trevor Martin and
Matthias Nickles and
Michael Pool},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, Germany},
  number = {900},
  pages = {3-14},
  pdf = {http://ceur-ws.org/Vol-900/paper1.pdf},
  abstract = {We present DISPONTE, a semantics for probabilistic ontologies that is based on the distribution semantics for probabilistic logic programs. In DISPONTE the axioms of a probabilistic ontology can be
annotated with an epistemic or a statistical probability. The epistemic probability represents a degree of confidence in the axiom, while the statistical probability considers the populations to which the axiom is applied.}
}
@comment{{This file has been generated by bib2bib 1.98}}
@comment{{Command line: bin/bib2bib -ob temp/2013.bib -c 'author:"Zese" or (editor:"Zese" and $key:"EB$")' bib_files/2013.bib}}
@comment{{This file has been generated by bib2bib 1.98}}
@comment{{Command line: bin/bib2bib -ob temp/2014.bib -c 'author:"Zese" or (editor:"Zese" and $key:"EB$")' bib_files/2014.bib}}
@inproceedings{Zese14-AIXIA14-IW,
  author = {Riccardo Zese},
  editor = {Luigi Di Caro and
               Carmine Dodaro and
               Andrea Loreggia and
               Roberto Navigli and
               Alan Perotti and
               Manuela Sanguinetti},
  title = {Learning Probabilistic Description Logics Theories},
  booktitle = {Proceedings of the Second Doctoral Workshop in Artificial Intelligence
               {(DWAI} 2014) An official workshop of the 13th Symposium of the Italian
               Association for Artificial Intelligence "Artificial Intelligence for
               Society and Economy" (AI*IA 2014), Pisa, Italy, December 11, 2014.},
  series = {{CEUR} Workshop Proceedings},
  volume = {1334},
  pages = {13--22},
  publisher = {CEUR-WS.org},
  year = {2014},
  url = {http://ceur-ws.org/Vol-1334/paper2.pdf}
}
@comment{{This file has been generated by bib2bib 1.98}}
@comment{{Command line: bin/bib2bib -ob temp/2015.bib -c 'author:"Zese" or (editor:"Zese" and $key:"EB$")' bib_files/2015.bib}}
@inproceedings{CotZes15-AIIADC-IW,
  title = {Learning Probabilistic Ontologies with Distributed Parameter Learning },
  author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
  pages = {7--12},
  pdf = {http://ceur-ws.org/Vol-1485/paper2.pdf},
  booktitle = {Proceedings of the Doctoral Consortium (DC)
co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)},
  year = 2015,
  editor = {Elena Bellodi and Alessio Bonfietti},
  volume = 1485,
  series = {CEUR Workshop Proceedings},
  address = {Aachen, Germany},
  issn = {1613-0073},
  venue = {Ferrara, Italy},
  eventdate = {2015-09-23/24},
  publisher = {Sun {SITE} Central Europe},
  copyright = {by the authors},
  abstract = {
We consider the problem of learning both the structure and
the parameters of Probabilistic Description Logics under DISPONTE.
DISPONTE (‚ÄúDIstribution Semantics for Probabilistic ONTologiEs‚ÄĚ)
adapts the distribution semantics for Probabilistic Logic Programming
to Description Logics. The system LEAP for "LEArning Probabilistic
description logics" learns both the structure and the parameters of
DISPONTE knowledge bases (KBs) by exploiting the algorithms CELOE
and EDGE. The former stands for "Class Expression Learning for Ontology
Engineering" and it is used to generate good candidate axioms
to add to the KB, while the latter learns the probabilistic parameters
and evaluates the KB. EDGE for "Em over bDds for description loGics
paramEter learning" is an algorithm for learning the parameters of probabilistic
ontologies from data. In order to contain the computational cost,
a distributed version of EDGE called EDGEMR was developed. EDGEMR
exploits the MapReduce (MR) strategy by means of the Message Passing
Interface. In this paper we propose the system LEAPMR. It is a
re-engineered version of LEAP which is able to use distributed parallel
parameter learning algorithms such as EDGEMR.
},
  keywords = {Probabilistic Description Logics, Structure Learning,
Parameter Learning, MapReduce, Message Passing Interface.
}
}
@inproceedings{ZesBel15-AIIADC-IW,
  title = {Tableau Reasoners for Probabilistic Ontologies Exploiting Logic Programming Techniques},
  author = {Riccardo Zese and Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
  pages = {1--6},
  pdf = {http://ceur-ws.org/Vol-1485/paper1.pdf},
  booktitle = {Proceedings of the Doctoral Consortium (DC)
co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)},
  year = 2015,
  editor = {Elena Bellodi and Alessio Bonfietti},
  volume = 1485,
  series = {CEUR Workshop Proceedings},
  address = {Aachen, Germany},
  issn = {1613-0073},
  venue = {Ferrara, Italy},
  eventdate = {2015-09-23/24},
  publisher = {Sun {SITE} Central Europe},
  copyright = {by the authors},
  abstract = {The adoption of Description Logics for modeling real world
domains within the Semantic Web is exponentially increased in the last
years, also due to the availability of a large number of reasoning algorithms.
Most of them exploit the tableau algorithm which has to manage
non-determinism, a feature that is not easy to handle using procedural
languages such as Java or C++. Reasoning on real world domains also
requires the capability of managing probabilistic and uncertain information.
We thus present TRILL, for "Tableau Reasoner for descrIption
Logics in proLog" and TRILLP
, for "TRILL powered by Pinpointing
formulas", which implement the tableau algorithm and return the probability
of queries. TRILLP
, instead of the set of explanations for a query,
computes a Boolean formula representing them, speeding up the computation.
},
  keywords = {Distribution Semantics, Probabilistic Semantic Web,
Logic Programming, Description Logics}
}
@inproceedings{CotZesBel15-ECMLDC-IW,
  year = {2015},
  booktitle = {Doctoral Consortium of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  editor = {Jaakko Hollmen and Panagiotis Papapetrou },
  title = {Structure Learning with Distributed Parameter
Learning for Probabilistic Ontologies},
  author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
  pages = {75--84},
  copyright = {by the authors},
  url = {http://urn.fi/URN:ISBN:978-952-60-6443-7},
  pdf = {https://aaltodoc.aalto.fi/bitstream/handle/123456789/18224/isbn9789526064437.pdf#page=79},
  isbn = {978-952-60-6443-7},
  issn = {1799-490X},
  issn = {1799-4896},
  abstract = {We consider the problem of learning both the structure and
the parameters of Probabilistic Description Logics under DISPONTE.
DISPONTE ("DIstribution Semantics for Probabilistic ONTologiEs")
adapts the distribution semantics for Probabilistic Logic Programming
to Description Logics. The system LEAP for "LEArning Probabilistic
description logics" learns both the structure and the parameters of
DISPONTE knowledge bases (KBs) by exploiting the algorithms CELOE
and EDGE. The former stands for "Class Expression Learning for Ontology
Engineering" and it is used to generate good candidate axioms
to add to the KB, while the latter learns the probabilistic parameters
and evaluates the KB. EDGE for "Em over bDds for description loGics
paramEter learning" is an algorithm for learning the parameters of probabilistic
ontologies from data. In order to contain the computational cost,
a distributed version of EDGE called EDGEMR was developed. EDGEMR
exploits the MapReduce (MR) strategy by means of the Message Passing
Interface. In this paper we propose the system LEAPMR. It is a
re-engineered version of LEAP which is able to use distributed parallel
parameter learning algorithms such as EDGEMR.},
  keywords = {Probabilistic Description Logics, Structure Learning,
Parameter Learning, MapReduce, Message Passing Interface}
}
@inproceedings{ZesBel15-OntoLP-IW,
  author = {Riccardo Zese and Elena Bellodi and Evelina Lamma and Fabrizio Riguzzi},
  title = {Logic Programming Techniques for Reasoning with Probabilistic Ontologies},
  booktitle = {International Workshop on Ontologies and Logic Programming for Query Answering},
  editor = {Odile Papini and Salem Benferhat and Laurent Garcia and
Marie-Laure Mugnier},
  year = {2015},
  url = {http://ds.ing.unife.it/~friguzzi/Papers/ZesBel-OntoLP15.pdf},
  pdf = {http://ontolp.lsis.org/files/pdf/proc-ontolp.pdf#page=13},
  keywords = {Description Logics, Tableau, Prolog, Semantic Web, Pinpoiting Formula},
  abstract = {The increasing popularity of the Semantic Web drove to a widespread 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 has to manage non-determinism,
  a feature that is hard to handle using such languages. Reasoning on real world domains also requires the capability of managing probabilistic and uncertain information.
  We thus present TRILL for ``Tableau Reasoner for descrIption Logics in proLog'' that implements a tableau algorithm and is able to return explanations for the queries and the corresponding probability, 
  and TRILL$^P$ for ``TRILL powered by Pinpointing formulas'' which is able to compute a Boolean formula representing the set of explanations for the query. This approach can speed up 
  the process of computing the probability.
  Prolog non-determinism is used for easily handling the tableau's non-deterministic expansion rules.},
  copyright = {by the authors}
}
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@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}
}
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@inproceedings{AlbLamRig17-PLP-IW,
  author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese},
  title = {A Distribution Semantics for non-{DL}-Safe Probabilistic Hybrid Knowledge Bases},
  booktitle = {4th International Workshop on Probabilistic logic programming, PLP 2017},
  editor = {Christian {Theil Have} and Riccardo Zese},
  year = {2017},
  pdf = {http://ceur-ws.org/Vol-1916/paper4.pdf},
  volume = 1916,
  series = {CEUR Workshop Proceedings},
  address = {Aachen, Germany},
  issn = {1613-0073},
  publisher = {Sun {SITE} Central Europe},
  pages = {40-50},
  scopus = {2-s2.0-85030093850},
  abstract = {Logic Programming languages and Description Logics are
based on different domain closure assumptions, closed and the open
world assumption, respectively. Since many domains require both these
assumptions, the combination of LP and DL have become of foremost importance.
An especially successful approach is based on Minimal Knowledge
with Negation as Failure (MKNF), whose semantics is used to define
Hybrid KBs, composed of logic programming rules and description logic
axioms. Following such idea, we have proposed an approach for defining
DL-safe Probabilistic Hybrid Knowledge Bases, where each disjunct in
the head of LP clauses and each DL axiom is annotated with a probability
value, following the well known distribution semantics. In this paper,
we show that this semantics can be unintuitive for non-DL-safe PHKBs,
and we propose a new semantics that coincides with the previous one if
the PHKB is DL-safe.},
  keywords = {Hybrid Knowledge Bases, MKNF, Distribution Semantics}
}
@inproceedings{RigLamAlb17-URANIA-IW,
  title = {Probabilistic Logic Programming for Natural Language Processing },
  author = {Fabrizio Riguzzi and Evelina Lamma and Marco Alberti and Elena Bellodi and Riccardo Zese and Giuseppe Cota},
  pages = {30--37},
  url = {http://ceur-ws.org/Vol-1802/},
  pdf = {http://ceur-ws.org/Vol-1802/paper4.pdf},
  booktitle = {{URANIA} 2016,
Deep Understanding and Reasoning: A Challenge for Next-generation Intelligent Agents,
Proceedings of the {AI*IA} Workshop on Deep Understanding and Reasoning: A Challenge for Next-generation Intelligent Agents 2016
co-located with 15th International Conference of the Italian Association for Artificial Intelligence ({AIxIA} 2016)},
  year = 2017,
  editor = {Federico Chesani and Paola Mello and Michela Milano},
  volume = 1802,
  series = {CEUR Workshop Proceedings},
  address = {Aachen, Germany},
  issn = {1613-0073},
  venue = {Genova, Italy},
  eventdate = {2016-11-28},
  publisher = {Sun {SITE} Central Europe},
  copyright = {by the authors},
  abstract = {The ambition of Artificial Intelligence is to solve problems without human intervention. Often the problem description is given in human (natural) language. Therefore it is crucial to find an automatic way to understand a text written by a human. The research field concerned with the interactions between computers and natural languages is known under the name of Natural Language Processing (NLP), one of the most studied fields of Artificial Intelligence.

In this paper we show that Probabilistic Logic Programming (PLP) is a suitable approach for NLP in various scenarios. For this purpose we use \texttt{cplint} on SWISH, a web application for Probabilistic Logic Programming. \texttt{cplint} on SWISH allows users to perform inference and learning with the framework \texttt{cplint} using just a web browser, with the computation performed on the server.},
  keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Natural Language Processing},
  scopus = {2-s2.0-85015943369}
}
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