workshops.bib

@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{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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