2007.bib

@inproceedings{Rig-ILP06-IC,
  author = {
 Fabrizio Riguzzi},
  title = {{ALLPAD}: Approximate Learning of Logic Programs with Annotated Disjunctions},
  booktitle = {Proceedings of the 16th International Conference on Inductive Logic Programming},
  year = {2007},
  editor = {Stephen Muggleton and Ramon Otero},
  publisher = {Springer},
  series = {Lecture Notes in Artificial Intelligence},
  abstract = {In this paper we present the system ALLPAD for learning Logic Programs with Annotated Disjunctions (LPADs).
 ALLPAD modifies  the  previous system LLPAD in order to  tackle real world learning problems more effectively. 
 This is achieved by looking for an approximate solution rather than a perfect one. ALLPAD has been tested on the
  problem of classifying proteins according  to their tertiary structures and the results compare favorably with
	 most other approaches.},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  volume = {4455},
  pages = {43--45},
  abstract = {In this paper we present the system ALLPAD for learning Logic Programs with Annotated Disjunctions (LPADs). ALLPAD modifies  the  previous system LLPAD in order to  tackle real world learning problems more effectively. This is achieved by looking for an approximate solution rather than a perfect one. ALLPAD has been tested on the problem of classifying proteins according  to their tertiary structures and the results compare favorably with most other approaches.},
  keywords = {Logic Programs with Annotated Disjunctions, Statistical Relational Learning},
  pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-ILP06.pdf},
  url = {http://link.springer.com/chapter/10.1007%2F978-3-540-73847-3_11},
  doi = {10.1007/978-3-540-73847-3_11},
  address = {Heidelberg, \Germany},
  copyright = {Springer}
}
@inproceedings{LamMelMonRigSto-BPM07-IC,
  author = {
 Evelina Lamma and Paola Mello and Marco Montali and Fabrizio Riguzzi and Sergio Storari},
  title = {Inducing Declarative Logic-Based Models from Labeled Traces},
  booktitle = {Proceedings of the 5th International Conference on Business Process Management},
  year = {2007},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  pdf = {http://mcs.unife.it/~friguzzi/Papers/LamMelMon-BPM07.pdf},
  http = {http://link.springer.com/chapter/10.1007%2F978-3-540-75183-0_25},
  doi = {10.1007/978-3-540-75183-0_25},
  volume = {4714},
  pages = {344--359},
  address = {Heidelberg, \Germany},
  copyright = {Springer},
  note = {The original publication is available at \url{http://www.springerlink.com}}
}
@inproceedings{LamRigStoMelMon-IPM07-IW,
  author = {
 Evelina Lamma and Fabrizio Riguzzi and Sergio Storari and Paola Mello and Marco Montali  },
  title = {Learning DecSerFlow Models  from Labeled Traces},
  booktitle = {Proceedings of the 1st International Workshop on the Induction of Process Models},
  year = {2007},
  pdf = {http://mcs.unife.it/~friguzzi/Papers/LamRigStoMelMon-IPM07.pdf},
  abstract = {We present the system DecMiner that induces DecSerFlow models from positive and negative traces. The approach we follow consists in first inducing SCIFF constraints and then converting them into DecSerFlow ones.
},
  keywords = {Process mining, Process verification and validation, Logic Programming, DecSerFlow, Careflow}
}
@inproceedings{Rig-AIIA07-IC,
  author = {
 Fabrizio Riguzzi },
  title = {A Top Down Interpreter for {LPAD} and {CP}\--logic},
  booktitle = {Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence},
  year = {2007},
  publisher = {Springer},
  address = {Heidelberg, \Germany},
  pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-AIIA07.pdf},
  url = {http://link.springer.com/chapter/10.1007%2F978-3-540-74782-6_11},
  series = {Lecture Notes in Artificial Intelligence},
  volume = {4733},
  note = {The original publication is available at \url{http://www.springerlink.com}},
  pages = {109--120},
  abstract = {Logic Programs with Annotated Disjunctions and CP-logic are two different but related languages for expressing probabilistic information in logic programming.
The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages. 
The algorithm is based on the one available for ProbLog.
The performances of the algorithm are compared with those of a Bayesian reasoner and with those of the ProbLog interpreter. On programs that have a small grounding, the Bayesian reasoner is more scalable, but programs with a large grounding require the top down interpreter. The comparison with ProbLog shows that the added expressiveness effectively requires more computation resources.},
  doi = {10.1007/978-3-540-74782-6_11 },
  keywords = {Probabilistic Logic Programming, Logic Programs with Annotated Disjunction, Probabilistic Reasoning},
  copyright = {Springer},
  scopus = {2s2.038049160383},
  wos = {WOS:000250857400009},
  isbn = {9783540747819},
  issn = {03029743}
}
@inproceedings{Rig-RCRA07-IW,
  author = {
 Fabrizio Riguzzi },
  title = {A Top Down Interpreter for {LPAD} and {CP}\--logic},
  booktitle = {Proceedings of the  14th RCRA workshop
Experimental Evaluation of Algorithms for 
Solving Problems with Combinatorial Explosion},
  year = {2007},
  pdf = {http://pst.istc.cnr.it/RCRA07/articoli/P19-riguzzi-RCRA07.pdf},
  abstract = {Logic Programs with Annotated Disjunctions and CP-logic are two different but related languages for expressing probabilistic information in logic programming.
The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages when the program is acyclic. 
The algorithm is based on the one available for ProbLog.
The performances of the algorithm are compared with those of a Bayesian reasoner and with those of the ProbLog interpreter. On programs that have a small grounding, the Bayesian reasoner is more scalable, but programs with a large grounding require the top down interpreter. The comparison with ProbLog shows that, even if the added expressiveness effectively requires more computation resources, the top down interpreter can still solve 
problem of significant size.
},
  keywords = {Probabilistic Logic Programming, Logic Programs with Annotated Disjunction, Probabilistic Reasoning}
}
@inproceedings{Rig-MRDM07-IW,
  author = {
 Fabrizio Riguzzi },
  title = {Learning Ground ProbLog Programs from Interpretations},
  booktitle = {Proceedings of the 6th Workshop on Multi-Relational Data Mining ({MRDM07})},
  year = {2007},
  pdf = {http://mcs.unife.it/~friguzzi/Papers/Rig-MRDM07.pdf},
  keywords = {Probabilistic Logical Models, ProbLog, LPAD, Noisy Or},
  abstract = {The relations between ProbLog and Logic Programs with Annotated Disjunctions imply that Boolean Bayesian networks can be represented as ground ProbLog programs and acyclic ground ProbLog programs can be represented as Boolean Bayesian networks. This provides a way of learning ground acyclic ProbLog programs from interpretations: first the interpretations are represented in tabular form, then a Bayesian network learning algorithm is applied and the learned network is translated into a ground ProbLog program. The program is then further analyzed in order to identify noisy or relations in it.
The paper proposes an algorithm for such identification and presents an experimental analysis of its computational complexity.}
}
@inproceedings{GamLamRigStoSca-DMFG07-IW,
  author = {
Giacomo Gamberoni and Evelina Lamma and Fabrizio Riguzzi and Sergio Storari and Chiara Scapoli },
  title = {Combining APRIORI and Bootstrap Techniques for Marker Analysis},
  booktitle = {Proceedings of the Workshop Data Mining in Functional Genomics and Proteomics: Current Trends and Future Directions},
  year = {2007},
  pdf = {http://mcs.unife.it/~friguzzi/Papers/GamLamRigScaSto-DMFG07.pdf},
  abstract = {In genetic studies, complex diseases are often analyzed searching for
marker patterns that play a significant role in the susceptibility to the
disease. 
In this paper we consider a dataset regarding periodontitis, that includes the analysis of nine genetic markers for 148 individuals.
We analyze these data by using a novel subgroup discovering algorithm, named APRIORI-B, that is based on APRIORI and bootstrap techniques. This algorithm can use different metrics for rule selection.
Experiments conducted by using as rule metrics novelty and confirmation, confirmed some previous results published on periodontitis.},
  keywords = {Data Mining, Functional Genomics, Marker Analysis, Periodontitis}
}

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