conferences.bib

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@inproceedings{RigBelLamZes13-AIIA13-IC,
  title = {Computing Instantiated Explanations {in~OWL~DL}},
  author = { Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and  Riccardo Zese},
  booktitle = {Proceedings of the 13th Conference of the Italian Association for Artificial Intelligence ({AI*IA2013}),
Turin, Italy,  4-6 December 2013},
  editor = {Matteo Baldoni and
Cristina Baroglio and Guido Boella},
  year = {2013},
  pages = {397-408},
  volume = {8249},
  publisher = {Springer},
  copyright = {Springer},
  series = {Lecture Notes in Artificial Intelligence},
  address = {Heidelberg, Germany},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigBelLamZes-AIIA13.pdf},
  note = {The original publication is available at 
\url{http://www.springerlink.com}},
  doi = {10.1007/978-3-319-03524-6_34}
}
@inproceedings{RigBelLamZese13-RR13b-IC,
  title = {{BUNDLE}: A Reasoner for Probabilistic Ontologies},
  author = {Fabrizio Riguzzi and Evelina Lamma and Elena Bellodi and Riccardo Zese},
  booktitle = {7th International Conference on Web Reasoning and Rule Systems (RR 2013), Mannheim, Germany, July 27-29 2013. Proceedings},
  editor = {Faber, Wolfgang and Lembo, Domenico},
  year = {2013},
  volume = {7994},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  address = {Heidelberg, Germany},
  isbn = {978-3-642-39665-6},
  copyright = {Springer},
  pages = {183-197},
  doi = {10.1007/978-3-642-39666-3_14},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigBelLam-RR13b.pdf},
  abstract = {
Representing uncertain information is very important for modeling real world domains. 
Recently, the DISPONTE semantics has been proposed for probabilistic description logics. 
In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version of the axiom in which the specified variables are instantiated.
In this paper we  present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that follow the $\mathcal{ALC}$ semantics. BUNDLE exploits an underlying  DL reasoner, such as Pellet, that is able to return explanations for  queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. 
The experiments performed by applying BUNDLE to probabilistic knowledge bases show that it can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-$\mathcal{SHIQ}$(D).
},
  keywords = {Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics},
  note = {The original publication is available at 
\url{http://www.springerlink.com}}
}
@inproceedings{RigBelLamZese13-RR13a-IC,
  author = {Fabrizio Riguzzi and Elena Bellodi and  Evelina Lamma  and Riccardo Zese},
  title = {Parameter Learning for Probabilistic Ontologies},
  booktitle = {7th International Conference on Web Reasoning and Rule Systems (RR 2013), Mannheim, Germany, July 27-29 2013. Proceedings},
  editor = {Faber, Wolfgang and Lembo, Domenico},
  year = {2013},
  volume = {7994},
  isbn = {978-3-642-39665-6},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  address = {Heidelberg, Germany},
  copyright = {Springer},
  pages = {265-270},
  doi = {10.1007/978-3-642-39666-3_26},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigBelLam-RR13a.pdf},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  abstract = {Recently, the problem of representing uncertainty in Description Logics (DLs) has received an increasing attention.
In probabilistic DLs, axioms contain numeric parameters that are often difficult to specify or to tune for a human.
In this paper we present an approach for learning and tuning the parameters of 
probabilistic ontologies from data. The resulting algorithm, 
called EDGE, for Em over bDds for description loGics paramEter learning,
is targeted to DLs following the DISPONTE approach, 
that applies the distribution semantics to DLs.},
  keywords = {Statistical Relational Learning, Probabilistic Inductive Logic Programming, Probabilistic Logic Programming,  Expectation Maximization, Binary Decision Diagrams,
 Logic Programs with Annotated Disjunctions}
}
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@inproceedings{RigBelLamZes14-ILP13-IC,
  author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
  title = {Learning the Parameters of Probabilistic Description Logics},
  booktitle = { Late Breaking papers of the 23rd International Conference on Inductive Logic Programming, 
Rio de Janeiro, Brazil,  August 28th to 30th, 2013},
  editor = {Gerson Zaverucha and Santos Costa, Vitor and Aline Marins Paes},
  year = {2014},
  volume = {1187},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  address = {Aachen, Germany},
  issn = {1613-0073},
  url = {http://ceur-ws.org/Vol-1187/paper-08.pdf},
  pages = {46-51},
  copyright = {by the authors}
}
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@inproceedings{GavLamRig15-ICLP-IC,
  editor = {De Vos, Marina and
Thomas Eiter and
Yuliya Lierler and
Francesca Toni},
  title = {An abductive Framework for {Datalog+-} Ontologies},
  author = {Marco Gavanelli and Evelina Lamma and  Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota},
  booktitle = {Technical Communications of the 31st  Int'l.
Conference on Logic Programming (ICLP 2015)},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  issn = {1613-0073},
  address = {Aachen, Germany},
  copyright = {by the authors},
  year = {2015},
  keywords = {Probabilistic Logic Programming, Lifted Inference, 
  Variable Elimination, Distribution Semantics, ProbLog, 
  Statistical Relational Artificial Intelligence},
  abstract = {
Ontologies  are a fundamental component of the Semantic Web since they provide a formal and machine manipulable model of a domain.
Description Logics (DLs) are often the languages of choice for modeling ontologies. Great effort has been spent in identifying decidable or even tractable fragments of DLs.  Conversely, for knowledge representation and reasoning,  integration with rules and rule-based reasoning is crucial in the so-called Semantic Web stack vision. 
Datalog+- is an extension of Datalog which can be used for representing lightweight ontologies, and is able to express 
the DL-Lite family  of ontology languages, with tractable query answering under certain language restrictions. 

In this work, we show that Abductive Logic Programming (ALP) is also a suitable framework for representing Datalog+- ontologies, supporting query answering through an abductive proof procedure, and smoothly achieving the integration of ontologies and rule-based reasoning. 
In particular, we consider an Abductive Logic Programming framework  named 
SCIFF, and  derived from the IFF abductive framework, able to deal with  existentially (and universally) quantified variables in rule heads, and Constraint Logic Programming  constraints. 
Forward and backward reasoning is naturally supported in the ALP framework. We show that the SCIFF language smoothly supports the integration of rules, expressed in a Logic Programming language, with Datalog+- ontologies,  mapped  into SCIFF (forward) integrity constraints. 
  },
  keywords = {Abductive Logic Programming, Datalog+-,
Description Logics,
Semantic Web.},
  number = {1433},
  url = {http://ceur-ws.org/Vol-1433/tc_89.pdf}
}
@inproceedings{RigBel15-IJCAI-IC,
  author = {Fabrizio Riguzzi and Elena Bellodi and Evelina Lamma and Riccardo Zese},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence,
Buenos Aires, Argentina, 25-31 July 2015},
  title = {Reasoning with Probabilistic Ontologies},
  year = {2015},
  editor = {Qiang Yang and Michael Wooldridge},
  pages = {4310-4316},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  address = {Palo Alto, California USA},
  copyright = {International Joint Conferences on Artificial Intelligence },
  isbn = {978-1-57735-738-4},
  pdf = {http://ijcai.org/papers15/Papers/IJCAI15-613.pdf},
  keywords = {Probabilistic Ontologies, Probabilistic Description Logics, OWL, Probabilistic Logic Programming, Distribution Semantics},
  abstract = {Modeling real world domains requires ever more frequently to represent uncertain information. 
The DISPONTE semantics for probabilistic description logics allows to annotate axioms of a knowledge base with a value that represents their probability.
In this paper we discuss approaches for performing inference 
from probabilistic ontologies following the DISPONTE semantics.
We present the algorithm BUNDLE for computing the probability of queries. 
BUNDLE exploits an underlying  Description Logic reasoner, such as Pellet, in order to find explanations for a query. These are then encoded in a Binary Decision Diagram that is
used for computing the probability of the query.}
}
@inproceedings{Zese15-IJCAI-IC,
  author = {Riccardo Zese},
  editor = {Qiang Yang and
               Michael Wooldridge},
  title = {Inference and Learning for Probabilistic Description Logics},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence,
Buenos Aires, Argentina, 25-31 July 2015},
  pages = {4411--4412},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  address = {Palo Alto, California USA},
  copyright = {International Joint Conferences on Artificial Intelligence },
  year = {2015},
  url = {http://ijcai.org/Abstract/15/653},
  pdf = {http://ds.ing.unife.it/~rzese/Papers/Zese15-IJCAI.pdf}
}
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@inproceedings{RigBelZes16-ECAI-IC,
  year = {2016},
  booktitle = {22nd European Conference  on Artificial Intelligence {ECAI 2016}},
  venue = {The Hague, Netherlands},
  eventdate = {August 29-September 2, 2016},
  editor = {Maria Fox and Gal Kaminka},
  title = {Scaling Structure Learning of Probabilistic Logic Programs by MapReduce},
  author = {Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese and Giuseppe Cota and Evelina Lamma},
  abstract = {Probabilistic Logic Programming is a promising formalism for dealing with uncertainty. Learning probabilistic logic programs has been receiving an increasing attention in Inductive Logic Programming: for instance,
the system SLIPCOVER learns high quality theories in a variety of domains. However, SLIPCOVER is computationally expensive, with a running time of the order of hours.
In order to apply SLIPCOVER to Big Data, we present SEMPRE, for ``Structure lEarning by MaPREduce", that scales SLIPCOVER by following a MapReduce strategy, directly implemented with the Message Passing Interface. },
  keywords = {Probabilistic Logic Programming, Parameter Learning, Structure Learning, MapReduce},
  series = {Frontiers in Artificial Intelligence and Applications},
  volume = {285},
  pages = {1602-1603},
  url = {http://ebooks.iospress.nl/volumearticle/44940},
  doi = {10.3233/978-1-61499-672-9-1602},
  wos = {WOS:000385793700205},
  scopus = {2-s2.0-85013029084},
  copyright = {CC-BY-NC 4.0},
  publisher = {IOS Press}
}
@inproceedings{AlbCotRigZes16-AIIA-IC,
  booktitle = {Proceedings of the 15th Conference of the Italian Association for Artificial Intelligence ({AI*IA2016}),
Genova, Italy,  28 November - 1 December 2016},
  editor = {Giovanni Adorni and Stefano Cagnoni and Marco Gori and Marco Maratea},
  year = {2016},
  title = {Probabilistic Logical Inference On the Web},
  author = {Marco Alberti and Giuseppe Cota and Fabrizio Riguzzi and Riccardo Zese},
  abstract = {cplint on SWISH is a web application for probabilistic
    logic programming. It allows users to perform inference and
    learning using just a web browser, with the computation performed
    on the server. In this paper we report on recent advances in the
    system, namely the inclusion of algorithms for computing
    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 is used that makes it possible to also have evidence on
    continuous variables. cplint on SWISH offers also the
    possibility of sampling arguments of goals, a kind of inference
    rarely considered but useful especially when the arguments are
    continuous variables. Finally, cplint on SWISH offers the
    possibility of graphing the results, for example by drawing the
    distribution of the sampled continuous arguments of goals.},
  publisher = {Springer International Publishing},
  address = {Heidelberg, Germany},
  series = {Lecture Notes in Computer Science},
  volume = {10037},
  copyright = {Springer International Publishing AG},
  keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Hybrid program},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/AlbCotRig-AIXIA16.pdf},
  doi = {10.1007/978-3-319-49130-1_26},
  pages = {351-363},
  venue = {Genova, Italy},
  eventdate = {November 28-December 1, 2016},
  isbn-online = {978-3-319-49129-5},
  isbn-print = {978-3-319-49130-1},
  issn = {0302-9743},
  scopus = {2-s2.0-85006074125},
  wos = {WOS:000389797400026},
  note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-49130-1_26}}
}
@inproceedings{AlbLamRigZes16-AIIA-IC,
  booktitle = {Proceedings of the 15th Conference of the Italian Association for Artificial Intelligence ({AI*IA2016}),
Genova, Italy,  28 November - 1 December 2016},
  editor = {Giovanni Adorni and Stefano Cagnoni and Marco Gori and Marco Maratea},
  year = {2016},
  title = {Probabilistic Hybrid Knowledge Bases under the Distribution
  Semantics},
  author = {Marco Alberti and Evelina Lamma and Fabrizio Riguzzi and Riccardo Zese},
  abstract = {Since Logic Programming (LP) and Description Logics (DLs) are based on
  different assumptions (the closed and the open world assumption,
  respectively), combining them provides higher expressiveness in
  applications  that require both
  assumptions.

  Several proposals have been made to combine LP and DLs. An especially
  successful line of research is the one based on the Lifschitz's
  logic of Minimal Knowledge with Negation as Failure (MKNF).  Motik
  and Rosati introduced Hybrid knowledge bases (KBs), composed of LP
  rules and DL axioms, gave them an MKNF semantics and
  studied their complexity. Knorr et al. proposed a well-founded semantics for
  Hybrid KBs where the LP clause heads are non-disjunctive, which
  keeps querying polynomial (provided the underlying DL is polynomial)
  even when the LP portion is non-stratified.

  In this paper, we propose Probabilistic Hybrid Knowledge Bases (PHKBs),
  where the atom in the head of LP clauses and each DL axiom is
  annotated with a probability value. PHKBs are given a distribution
  semantics by defining a probability distribution over deterministic
  Hybrid KBs. The probability of a query being true is the sum of the
  probabilities of the deterministic KBs that entail the query. Both
  epistemic and statistical probability can be addressed, thanks to
  the integration of probabilistic LP and DLs.},
  publisher = {Springer International Publishing},
  address = {Heidelberg, Germany},
  series = {Lecture Notes in Computer Science},
  volume = {10037},
  copyright = {Springer International Publishing AG},
  issn = {0302-9743},
  keywords = {Probabilistic Logic Programming, Probabilistic Description Logics, Hybrid Knowledge Bases},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/AlbLamRig-AIXIA16.pdf},
  doi = {10.1007/978-3-319-49130-1_27},
  pages = {364-376},
  venue = {Genova, Italy},
  scopus = {2-s2.0-85005950065},
  wos = {WOS:000389797400027},
  eventdate = {November 28-December 1, 2016},
  isbn-online = {978-3-319-49129-5},
  isbn-print = {978-3-319-49130-1},
  note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-49130-1_27}}
}
@inproceedings{CotZesBel16-ILP-IC,
  booktitle = {Inductive Logic Programming: 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers},
  editor = {Katsumi Inoue and Hayato Ohwada and Akihiro Yamamoto},
  title = {Distributed Parameter Learning for Probabilistic Ontologies},
  author = {Giuseppe Cota and Riccardo Zese and Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/CotZesBel-ILP15.pdf},
  year = {2016},
  publisher = {Springer International Publishing},
  address = {Heidelberg, Germany},
  series = {Lecture Notes in Computer Science},
  volume = {9575},
  copyright = {Springer International Publishing Switzerland},
  venue = {Kyoto, Japan},
  eventdate = {August 20-22, 2015},
  pages = {30--45},
  isbn-online = {978-3-319-40566-7},
  isbn-print = {978-3-319-40565-0},
  issn = {0302-9743},
  doi = {10.1007/978-3-319-40566-7_3},
  abstract = {Representing uncertainty in Description Logics has recently
received an increasing attention because of its potential to model real
world domains. EDGE for Em over bDds for description loGics param-
Eter learning is an algorithm for learning the parameters of probabilistic
ontologies from data. However, the computational cost of this algorithm
is significant since it may take hours to complete an execution. In this
paper we present EDGEMR, a distributed version of EDGE that exploits
the MapReduce strategy by means of the Message Passing Interface. Ex-
periments on various domains show that EDGEMR signicantly reduces
EDGE running time.},
  keywords = {Probabilistic Description Logics, Parameter Learning, MapReduce,
Message Passing Interface},
  note = {The final publication is available at Springer via
\url{http://dx.doi.org/10.1007/978-3-319-40566-7_3}}
}
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@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://ds.ing.unife.it/~friguzzi/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 qualified 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{Zes17-RR-IC,
  author = {Riccardo Zese},
  editor = {Nick Bassiliades and
               Antonis Bikakis and
               Stefania Costantini and
               Enrico Franconi and
               Adrian Giurca and
               Roman Kontchakov and
               Theodore Patkos and
               Fariba Sadri and
               William Van Woensel},
  title = {Probabilistic description logics: Reasoning and learning},
  booktitle = {Proceedings of the Doctoral Consortium, Challenge, Industry Track,
               Tutorials and Posters @ RuleML+RR 2017 hosted by International Joint
               Conference on Rules and Reasoning 2017 (RuleML+RR 2017), London, UK,
               July 11-15, 2017.},
  series = {{CEUR} Workshop Proceedings},
  volume = {1875},
  publisher = {CEUR-WS.org},
  year = {2017},
  url = {http://ceur-ws.org/Vol-1875/paper19.pdf}
}
@incollection{RigZesCot17-EKAW-IC,
  title = {Probabilistic Inductive Logic Programming on the Web},
  author = {Fabrizio Riguzzi and Riccardo Zese and Giuseppe Cota},
  booktitle = {20th International Conference on Knowledge Engineering and Knowledge Management,
  {EKAW} 2016; Bologna; Italy; 19 November 2016 through 23 November 2016},
  year = {2017},
  publisher = {Springer},
  address = {Cham},
  series = {Lecture Notes in Computer Science},
  volume = {10180},
  pdf = {http://ds.ing.unife.it/~friguzzi/Papers/RigZesCot-EKAW16.pdf},
  isbn-online = {978-3-319-58694-6},
  isbn-print = {978-3-319-58693-9},
  pages = {172--175},
  scopus = {2-s2.0-85019730114},
  doi = {10.1007/978-3-319-58694-6_25},
  abstract = {Probabilistic Inductive Logic Programming (PILP) is gaining attention for its
capability of modeling complex domains containing uncertain relationships among entities.
Among PILP systems, \texttt{cplint} provides inference and learning algorithms competitive with the state of the art. Besides parameter
learning, \texttt{cplint} provides one of the few structure learning
algorithms for PLP, SLIPCOVER.
Moreover, an online version was recently developed,  \texttt{cplint} on SWISH, that allows users to experiment with the system using just a
web browser.
In this demo we illustrate \texttt{cplint} on SWISH concentrating
on structure learning with SLIPCOVER.
\texttt{cplint} on SWISH also includes many examples and a step-by-step tutorial.},
  keywords = {Probabilistic Inductive Logic Programming, Probabilistic Logic Programming,
Inductive Logic Programming},
  copyright = {Springer International Publishing AG},
  note = {The final publication is available at Springer via
 \url{http://dx.doi.org/10.1007/978-3-319-58694-6_25}},
  venue = {Bologna, Italy},
  eventdate = {November 19-November 23, 2016}
}
@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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