author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese},
  title = {Iterated Fixpoint Well-founded Semantics for Hybrid Knowledge Bases},
  booktitle = {Joint Proceedings of the 18th Italian Conference on Theoretical Computer Science and
the 32nd Italian Conference on Computational Logic},
  eventdate = {26-28 September 2017},
  venue = {Naples, Italy},
  editor = {{Dario Della Monica} and Aniello Murano and Sasha Rubin and Luigi Sauro},
  year = {2017},
  series = {CEUR Workshop Proceedings},
  address = {Aachen, Germany},
  issn = {1613-0073},
  publisher = {Sun {SITE} Central Europe},
  pages = {248-261},
  pdf = {http://ceur-ws.org/Vol-1949/CILCpaper01.pdf},
  volume = 1949,
  abstract = {
MKNF-based Hybrid Knowledge Bases (HKBs) integrate Logic Programming (LP) and
Description Logics (DLs) offering the combined expressiveness of the two formalisms.
In particular, HKB allow to make different closure assumptions for different predicates.
HKBs have been given a well-founded semantics in terms of an alternate fixpoint.
In this paper we provide an alternative definition of the semantics using an
iterated fixpoint. In this way the computation of the well-founded model proceeds
uniformly bottom-up, making the semantics easier to understand, to reason with and to automate.
We also present slightly different but equivalent versions of our definition.
We then discuss the relationships of HKBs with other formalisms.
The results show that overall HKBs seem to be those that more tightly integrate LP and DL,
even if there exist incomparable languages such as the recent FO(ID) formalism.},
  keywords = {Hybrid Knowledge Bases, MKNF, Well-foudned semantics, Description Logics}
  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}
  author = {Alberti, Marco and Gavanelli, Marco
and Lamma, Evelina
and Riguzzi, Fabrizio
and Riccardo, Zese},
  editor = {Costantini, Stefania
and Franconi, Enrico
and Van Woensel, William
and Kontchakov, Roman
and Sadri, Fariba
and Roman, Dumitru},
  title = {Dischargeable Obligations in Abductive Logic Programming},
  booktitle = {Rules and Reasoning: International Joint Conference,
 RuleML+RR 2017, London, UK, July 12--15, 2017, Proceedings},
  year = {2017},
  publisher = {Springer International Publishing},
  copyright = {Springer International Publishing AG},
  series = {Lecture Notes in Computer Science},
  volume = {10364},
  address = {Cham},
  isbn-print = {978-3-319-61251-5},
  isbn-online = {978-3-319-61252-2},
  doi = {10.1007/978-3-319-61252-2_2},
  pdf = {http://ml.unife.it/wp-content/uploads/Papers/AlbGavLam-RR17.pdf},
  pages = {7--21},
  abstract = {
Abductive Logic Programming (ALP) has been proven very
effective for formalizing societies of agents, commitments and norms, in
particular by mapping the most common deontic operators (obligation,
prohibition, permission) to abductive expectations.
In our previous works, we have shown that ALP is a suitable framework
for representing norms. Normative reasoning and query answering were
accommodated by the same abductive proof procedure, named SCIFF.
In this work, we introduce a defeasible
flavour in this framework, in order
to possibly discharge obligations in some scenarios. Abductive expectations
can also be qualified as dischargeable, in the new, extended syntax.
Both declarative and operational semantics are improved accordingly,
and proof of soundness is given under syntax allowedness conditions.
The expressiveness and power of the extended framework, named SCIFFD,
is shown by modeling and reasoning upon a fragment of the Japanese
Civil Code. In particular, we consider a case study concerning manifestations
of intention and their rescission (Section II of the Japanese Civil
  keywords = {Abduction, Abductive Logic Programming, Legal Reasoning,
Normative Reasoning},
  note = {The final publication is available at Springer via
  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}
  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
  volume = {11},
  number = {1},
  doi = {10.3233/IA-170106},
  pages = {47--64},
  wos = {WOS:000399736500004}

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