publications | Francesco Guerra

Please refer to DBLP, Scopus and Google Scholar for the complete list.

2023

TKDE

Pushing ML Predictions Into DBMSs

Matteo Paganelli, Paolo Sottovia, Kwanghyun Park, and 2 more authors

IEEE Trans. Knowl. Data Eng. 2023

@article{DBLP:journals/tkde/PaganelliSPIG23,
  author = {Paganelli, Matteo and Sottovia, Paolo and Park, Kwanghyun and Interlandi, Matteo and Guerra, Francesco},
  title = {Pushing {ML} Predictions Into DBMSs},
  journal = {{IEEE} Trans. Knowl. Data Eng.},
  volume = {35},
  number = {10},
  pages = {10295--10308},
  year = {2023}
}

SEBD

Interpretable Entity Matching with WYM

Andrea Baraldi, Francesco Del Buono, Francesco Guerra, and 3 more authors

In SEBD 2023

Bib

@inproceedings{DBLP:conf/sebd/0002B0GPV23,
  author = {Baraldi, Andrea and Buono, Francesco Del and Guerra, Francesco and Guiduzzi, Giacomo and Paganelli, Matteo and Vincini, Maurizio},
  title = {Interpretable Entity Matching with {WYM}},
  booktitle = {{SEBD}},
  series = {{CEUR} Workshop Proceedings},
  volume = {3478},
  pages = {101--109},
  publisher = {CEUR-WS.org},
  year = {2023}
}

EDBT

An Intrinsically Interpretable Entity Matching System

Andrea Baraldi, Francesco Del Buono, Francesco Guerra, and 2 more authors

In EDBT 2023

Bib

@inproceedings{DBLP:conf/edbt/0002B0PV23,
  author = {Baraldi, Andrea and Buono, Francesco Del and Guerra, Francesco and Paganelli, Matteo and Vincini, Maurizio},
  title = {An Intrinsically Interpretable Entity Matching System},
  booktitle = {{EDBT}},
  pages = {645--657},
  publisher = {OpenProceedings.org},
  year = {2023}
}

2022

PVLDB

Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering

Angela Bonifati, Francesco Del Buono, Francesco Guerra, and 1 more author

Proc. VLDB Endow. 2022

Bib

@article{DBLP:journals/pvldb/BonifatiB0T22,
  author = {Bonifati, Angela and Buono, Francesco Del and Guerra, Francesco and Tiano, Donato},
  title = {Time2Feat: Learning Interpretable Representations for Multivariate
                 Time Series Clustering},
  journal = {Proc. {VLDB} Endow.},
  volume = {16},
  number = {2},
  pages = {193--201},
  year = {2022}
}

PVLDB

Analyzing How BERT Performs Entity Matching

Matteo Paganelli, Francesco Del Buono, Andrea Baraldi, and 1 more author

Proc. VLDB Endow. 2022

Bib HTML

@article{DBLP:journals/pvldb/PaganelliBBG22,
  author = {Paganelli, Matteo and Buono, Francesco Del and Baraldi, Andrea and Guerra, Francesco},
  title = {Analyzing How {BERT} Performs Entity Matching},
  journal = {Proc. {VLDB} Endow.},
  volume = {15},
  number = {8},
  pages = {1726--1738},
  year = {2022},
}

SAC

Evaluating the integration of datasets

Matteo Paganelli, Francesco Del Buono, Francesco Guerra, and 1 more author

In SAC 2022

Bib

@inproceedings{DBLP:conf/sac/PaganelliB0022,
  author = {Paganelli, Matteo and Buono, Francesco Del and Guerra, Francesco and Ferro, Nicola},
  title = {Evaluating the integration of datasets},
  booktitle = {{SAC}},
  pages = {347--356},
  publisher = {{ACM}},
  year = {2022}
}

SEBD

Landmark Explanation: a Tool for Entity Matching

Andrea Baraldi, Francesco Del Buono, Matteo Paganelli, and 1 more author

In SEBD 2022

Bib

@inproceedings{DBLP:conf/sebd/0002BP022,
  author = {Baraldi, Andrea and Buono, Francesco Del and Paganelli, Matteo and Guerra, Francesco},
  title = {Landmark Explanation: a Tool for Entity Matching},
  booktitle = {{SEBD}},
  series = {{CEUR} Workshop Proceedings},
  volume = {3194},
  pages = {69--76},
  publisher = {CEUR-WS.org},
  year = {2022}
}

2021

CIKM

Landmark Explanation: An Explainer for Entity Matching Models

Andrea Baraldi, Francesco Del Buono, Matteo Paganelli, and 1 more author

In CIKM 2021

Bib

@inproceedings{DBLP:conf/cikm/BaraldiBP021,
  author = {Baraldi, Andrea and Buono, Francesco Del and Paganelli, Matteo and Guerra, Francesco},
  title = {Landmark Explanation: An Explainer for Entity Matching Models},
  booktitle = {{CIKM}},
  pages = {4680--4684},
  publisher = {{ACM}},
  year = {2021},
}

EDBT

Automated Machine Learning for Entity Matching Tasks

Matteo Paganelli, Francesco Del Buono, Marco Pevarello, and 2 more authors

In EDBT 2021

Bib

@inproceedings{DBLP:conf/edbt/PaganelliBP0V21,
  author = {Paganelli, Matteo and Buono, Francesco Del and Pevarello, Marco and Guerra, Francesco and Vincini, Maurizio},
  title = {Automated Machine Learning for Entity Matching Tasks},
  booktitle = {{EDBT}},
  pages = {325--330},
  publisher = {OpenProceedings.org},
  year = {2021}
}

EDBT

Using Landmarks for Explaining Entity Matching Models

Andrea Baraldi, Francesco Del Buono, Matteo Paganelli, and 1 more author

In EDBT 2021

Bib

@inproceedings{DBLP:conf/edbt/BaraldiBP021,
  author = {Baraldi, Andrea and Buono, Francesco Del and Paganelli, Matteo and Guerra, Francesco},
  title = {Using Landmarks for Explaining Entity Matching Models},
  booktitle = {{EDBT}},
  pages = {451--456},
  publisher = {OpenProceedings.org},
  year = {2021}
}

SEBD

Using Descriptions for Explaining Entity Matches (Discussion Paper)

Matteo Paganelli, Paolo Sottovia, Antonio Maccioni, and 2 more authors

In SEBD 2021

Bib

@inproceedings{DBLP:conf/sebd/PaganelliSMI021,
  author = {Paganelli, Matteo and Sottovia, Paolo and Maccioni, Antonio and Interlandi, Matteo and Guerra, Francesco},
  title = {Using Descriptions for Explaining Entity Matches (Discussion Paper)},
  booktitle = {{SEBD}},
  series = {{CEUR} Workshop Proceedings},
  volume = {2994},
  pages = {291--298},
  publisher = {CEUR-WS.org},
  year = {2021}
}

SIGMOD

Transforming ML Predictive Pipelines into SQL with MASQ

Francesco Del Buono, Matteo Paganelli, Paolo Sottovia, and 2 more authors

In SIGMOD Conference 2021

Bib

@inproceedings{DBLP:conf/sigmod/BuonoPSI021,
  author = {Buono, Francesco Del and Paganelli, Matteo and Sottovia, Paolo and Interlandi, Matteo and Guerra, Francesco},
  title = {Transforming {ML} Predictive Pipelines into {SQL} with {MASQ}},
  booktitle = {{SIGMOD} Conference},
  pages = {2696--2700},
  publisher = {{ACM}},
  year = {2021}
}

EDBT

Proceedings of the 24th International Conference on Extending Database Technology, EDBT 2021, Nicosia, Cyprus, March 23 - 26, 2021

2021

Bib

@proceedings{DBLP:conf/edbt/2021,
  editor = {Velegrakis, Yannis and Zeinalipour{-}Yazti, Demetris and Chrysanthis, Panos K. and Guerra, Francesco},
  title = {Proceedings of the 24th International Conference on Extending Database
                 Technology, {EDBT} 2021, Nicosia, Cyprus, March 23 - 26, 2021},
  publisher = {OpenProceedings.org},
  year = {2021}
}

2020

iiWAS

Unsupervised Evaluation of Data Integration Processes

Matteo Paganelli, Francesco Del Buono, Francesco Guerra, and 1 more author

In iiWAS 2020

Bib

@inproceedings{DBLP:conf/iiwas/PaganelliB0020,
  author = {Paganelli, Matteo and Buono, Francesco Del and Guerra, Francesco and Ferro, Nicola},
  title = {Unsupervised Evaluation of Data Integration Processes},
  booktitle = {iiWAS},
  pages = {77--81},
  publisher = {{ACM}},
  year = {2020}
}

Inf.Syst.

Explaining data with descriptions

Matteo Paganelli, Paolo Sottovia, Antonio Maccioni, and 2 more authors

Inf. Syst. 2020

Bib

@article{DBLP:journals/is/PaganelliSMIG20,
  author = {Paganelli, Matteo and Sottovia, Paolo and Maccioni, Antonio and Interlandi, Matteo and Guerra, Francesco},
  title = {Explaining data with descriptions},
  journal = {Inf. Syst.},
  volume = {92},
  pages = {101549},
  year = {2020}
}

A Comparison of Approaches for Measuring the Semantic Similarity of Short Texts Based on Word Embeddings

Karlo Babic, Francesco Guerra, Sanda Martincic-Ipsic, and 1 more author

2020

Abs Bib

Measuring the semantic similarity of texts has a vital role in various tasks from the field of natural language processing. In this paper, we describe a set of experiments we carried out to evaluate and compare the performance of different approaches for measuring the semantic similarity of short texts. We perform a comparison of four models based on word embeddings: two variants of Word2Vec (one based on Word2Vec trained on a specific dataset and the second extending it with embeddings of word senses), FastText, and TF-IDF. Since these models provide word vectors, we experiment with various methods that calculate the semantic similarity of short texts based on word vectors. More precisely, for each of these models, we test five methods for aggregating word embeddings into text embedding. We introduced three methods by making variations of two commonly used similarity measures. One method is an extension of the cosine similarity based on centroids, and the other two methods are variations of the Okapi BM25 function. We evaluate all approaches on the two publicly available datasets: SICK and Lee in terms of the Pearson and Spearman correlation. The results indicate that extended methods perform better from the original in most of the cases.
@article{WOS:000597419200003, author = {Babic, Karlo and Guerra, Francesco and Martincic-Ipsic, Sanda and Mestrovic, Ana}, title = {A Comparison of Approaches for Measuring the Semantic Similarity of Short Texts Based on Word Embeddings}, journal = {JOURNAL OF INFORMATION AND ORGANIZATIONAL SCIENCES}, year = {2020}, volume = {44}, number = {2}, pages = {231-246}, type = {Article}, affiliation = {Babic, K (Corresponding Author), Univ Rijeka, Ctr Artificial Intelligence \& Cybersecur, Dept Informat, Rijeka, Croatia. Babic, Karlo; Martincic-Ipsic, Sanda; Mestrovic, Ana, Univ Rijeka, Ctr Artificial Intelligence \& Cybersecur, Dept Informat, Rijeka, Croatia. Guerra, Francesco, Univ Modena \& Reggio Emilia, Dept Engn Enzo Ferrari, Modena, Italy.}, doi = {10.31341/jios.44.2.2}, issn = {1846-3312}, eissn = {1846-9418}, author-email = {karlo.babic@uniri.hr francesco.guerra@unimore.it smarti@uniri.hr amestrovic@uniri.hr}, times-cited = {0}, unique-id = {WOS:000597419200003}, da = {2022-12-15} }