Entity Resolution is a core data integration task that relies on Blocking to scale to large datasets. Schema-agnostic blocking achieves very high recall, requires no domain knowledge and applies to data of any structuredness and schema heterogeneity. This comes at the cost of many irrelevant candidate pairs (i.e., comparisons), which can be significantly reduced by Meta-blocking techniques that leverage the entity co-occurrence patterns inside blocks: first, pairs of candidate entities are weighted in proportion to their matching likelihood, and then, pruning discards the pairs with the lowest scores. Supervised Meta-blocking goes beyond this approach by combining multiple scores per comparison into a feature vector that is fed to a binary classifier. By using probabilistic classifiers, Generalized Supervised Meta-blocking associates every pair of candidates with a score that can be used by any pruning algorithm. For higher effectiveness, new weighting schemes are examined as features. Through extensive experiments, we identify the best pruning algorithms, their optimal sets of features, as well as the minimum possible size of the training set.

Generalized Supervised Meta-blocking / Gagliardelli, Luca; Papadakis, George; Simonini, Giovanni; Bergamaschi, Sonia; Palpanas, Themis. - In: PROCEEDINGS OF THE VLDB ENDOWMENT. - ISSN 2150-8097. - 15:9(2022), pp. 1902-1910. ((Intervento presentato al convegno 48th International Conference on Very Large Data Bases, VLDB 2022 tenutosi a Sidney nel 5-9 settembre 2022 [10.14778/3538598.3538611].

Generalized Supervised Meta-blocking

Luca Gagliardelli
;
Giovanni Simonini;Sonia Bergamaschi;
2022

Abstract

Entity Resolution is a core data integration task that relies on Blocking to scale to large datasets. Schema-agnostic blocking achieves very high recall, requires no domain knowledge and applies to data of any structuredness and schema heterogeneity. This comes at the cost of many irrelevant candidate pairs (i.e., comparisons), which can be significantly reduced by Meta-blocking techniques that leverage the entity co-occurrence patterns inside blocks: first, pairs of candidate entities are weighted in proportion to their matching likelihood, and then, pruning discards the pairs with the lowest scores. Supervised Meta-blocking goes beyond this approach by combining multiple scores per comparison into a feature vector that is fed to a binary classifier. By using probabilistic classifiers, Generalized Supervised Meta-blocking associates every pair of candidates with a score that can be used by any pruning algorithm. For higher effectiveness, new weighting schemes are examined as features. Through extensive experiments, we identify the best pruning algorithms, their optimal sets of features, as well as the minimum possible size of the training set.
48th International Conference on Very Large Data Bases, VLDB 2022
Sidney
5-9 settembre 2022
15
1902
1910
Gagliardelli, Luca; Papadakis, George; Simonini, Giovanni; Bergamaschi, Sonia; Palpanas, Themis
Generalized Supervised Meta-blocking / Gagliardelli, Luca; Papadakis, George; Simonini, Giovanni; Bergamaschi, Sonia; Palpanas, Themis. - In: PROCEEDINGS OF THE VLDB ENDOWMENT. - ISSN 2150-8097. - 15:9(2022), pp. 1902-1910. ((Intervento presentato al convegno 48th International Conference on Very Large Data Bases, VLDB 2022 tenutosi a Sidney nel 5-9 settembre 2022 [10.14778/3538598.3538611].
File in questo prodotto:
File Dimensione Formato  
p2317-gagliardelli.pdf

accesso aperto

Tipologia: Pre-print dell'autore (bozza pre referaggio)
Dimensione 397.21 kB
Formato Adobe PDF
397.21 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1278618
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact