2) sources either treated the different sources as a single source or assumed that the individual sources are duplicate-free, so that only matches between sources have to be found. In this work we propose and evaluate a general Multi-Source Clean Dirty (MSCD) scheme with an arbitrary combination of clean (duplicate-free) and dirty sources. For this purpose, we extend a constraint-based clustering algorithm called Affinity Propagation (AP) for entity clustering with clean and dirty sources (MSCD-AP). We also consider a hierarchical version of it for improved scalability. Our evaluation considers a full range of datasets containing 0% to 100% of clean sources. We compare our proposed algorithms with other clustering schemes in terms of both match quality and runtime.">2) sources either treated the different sources as a single source or assumed that the individual sources are duplicate-free, so that only matches between sources have to be found. In this work we propose and evaluate a general Multi-Source Clean Dirty (MSCD) scheme with an arbitrary combination of clean (duplicate-free) and dirty sources. For this purpose, we extend a constraint-based clustering algorithm called Affinity Propagation (AP) for entity clustering with clean and dirty sources (MSCD-AP). We also consider a hierarchical version of it for improved scalability. Our evaluation considers a full range of datasets containing 0% to 100% of clean sources. We compare our proposed algorithms with other clustering schemes in terms of both match quality and runtime.">
Nothing Special   »   [go: up one dir, main page]
Logo des Repositoriums
 
Textdokument

Extended Affinity Propagation Clustering for Multi-source Entity Resolution

Vorschaubild

Volltext URI

Dokumententyp

Dateien

A3-1.pdf (2.87 MB)

Zusatzinformation

Datum

2021

Autor:innen

Zeitschriftentitel

ISSN der Zeitschrift

Bandtitel

Quelle

BTW 2021
Data Integration, Semantic Data Management, Streaming

Verlag

Gesellschaft für Informatik, Bonn

Zusammenfassung

Entity resolution is the data integration task of identifying matching entities (e.g. products, customers) in one or several data sources. Previous approaches for matching and clustering entities between multiple (>2) sources either treated the different sources as a single source or assumed that the individual sources are duplicate-free, so that only matches between sources have to be found. In this work we propose and evaluate a general Multi-Source Clean Dirty (MSCD) scheme with an arbitrary combination of clean (duplicate-free) and dirty sources. For this purpose, we extend a constraint-based clustering algorithm called Affinity Propagation (AP) for entity clustering with clean and dirty sources (MSCD-AP). We also consider a hierarchical version of it for improved scalability. Our evaluation considers a full range of datasets containing 0% to 100% of clean sources. We compare our proposed algorithms with other clustering schemes in terms of both match quality and runtime.

Beschreibung

Lerm, Stefan; Saeedi, Alieh; Rahm, Erhard (2021): Extended Affinity Propagation Clustering for Multi-source Entity Resolution. BTW 2021. DOI: 10.18420/btw2021-11. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-705-0. pp. 217-236. Data Integration, Semantic Data Management, Streaming. Dresden. 13.-17. September 2021

Schlagwörter

Entity Resolution , Clustering , Affinity Propagation , MSCD-AP

Zitierform

DOI

10.18420/btw2021-11

Tags

Sammlungen

P311 - BTW2021- Datenbanksysteme für Business, Technologie und Web