@inproceedings{AlbLamRig17-CILC-NC,
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}
}
@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{NguLamRig17-PLP-IW,
author = {Arnaud {Nguembang Fadja} and Evelina Lamma and Fabrizio Riguzzi},
title = {Deep Probabilistic Logic Programming},
booktitle = {Proceedings of the 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/paper1.pdf},
volume = 1916,
series = {CEUR Workshop Proceedings},
address = {Aachen, Germany},
issn = {1613-0073},
publisher = {Sun {SITE} Central Europe},
pages = {3-14},
abstract = {Probabilistic logic programming under the distribution
semantics has been very useful in machine learning. However, inference is
expensive so machine learning algorithms may turn out to be slow. In
this paper we consider a restriction of the language called hierarchical
PLP in which clauses and predicates are hierarchically organized. In this
case the language becomes truth-functional and inference reduces to the
evaluation of formulas in the product fuzzy logic. Programs in this
language can also be seen as arithmetic circuits or deep neural networks
and inference can be reperformed quickly when the parameters change.
Learning can then be performed by EM or backpropagation.},
keywords = {Probabilistic Logic Programming, Distribution Semantics, Deep
Neural Networks, Arithmetic Circuits},
scopus = {2-s2.0-85030091907},
venue = {Orleans, FR},
eventdate = {2017-09-07}
}
@inproceedings{ZamCanRig17-IMW-IW,
author = {Cristian Zambelli and Giuseppe Cancelliere and Fabrizio Riguzzi and Evelina Lamma and Piero Olivo and Alessia Marelli and Rino Micheloni},
booktitle = {2017 IEEE International Memory Workshop (IMW)},
title = {Characterization of {TLC 3D-NAND} Flash Endurance through Machine Learning for {LDPC} Code Rate Optimization},
year = {2017},
pages = {1-4},
keywords = {Clustering algorithms;Computer architecture;Error correction codes;Flash memories;Optimization;Parity check codes;Reliability},
doi = {10.1109/IMW.2017.7939074},
month = {May},
publisher = {IEEE},
venue = {Monterey, CA, USA},
eventdate = {14-17 May 2017}
}
@inproceedings{AlbGavLam17-RR-IC,
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
Code).},
keywords = {Abduction, Abductive Logic Programming, Legal Reasoning,
Normative Reasoning},
note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-61252-2_2}}
}
@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}
}
@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}
}
@inproceedings{GavLamRig17-JURISIN-IC,
author = {Gavanelli, Marco
and Lamma, Evelina
and Riguzzi, Fabrizio
and Bellodi, Elena
and Riccardo, Zese
and Cota, Giuseppe},
editor = {Otake, Mihoko
and Kurahashi, Setsuya
and Ota, Yuiko
and Satoh, Ken
and Bekki, Daisuke},
title = {Abductive Logic Programming for Normative Reasoning and Ontologies},
booktitle = {New Frontiers in Artificial Intelligence: JSAI-isAI 2015 Workshops,
LENLS, JURISIN, AAA, HAT-MASH, TSDAA, ASD-HR, and SKL, Kanagawa, Japan, November 16-18, 2015, Revised Selected Papers},
year = {2017},
publisher = {Springer International Publishing},
copyright = {Springer International Publishing AG},
series = {Lecture Notes in Computer Science},
volume = {10091},
address = {Cham},
pages = {187--203},
isbn-online = {978-3-319-50953-2},
isbn-print = {978-3-319-50952-5},
doi = {10.1007/978-3-319-50953-2_14},
scopus = {2-s2.0-85018397999}
}
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