%0 Conference Paper %1 Lerm2021-tr %A Lerm, Stefan %A Saeedi, Alieh %A Rahm, Erhard %D 2021 %I Gesellschaft für Informatik, Bonn %K %T Extended Affinity Propagation clustering for Multi-source entity resolution %X 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.

@inproceedings{Lerm2021-tr, abstract = {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.}, added-at = {2024-09-10T11:56:37.000+0200}, author = {Lerm, Stefan and Saeedi, Alieh and Rahm, Erhard}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2aca2d0304d8a1a8c6fa193149b5d6cc1/scadsfct}, interhash = {628fef9c1401aca725c8b4c467a161ef}, intrahash = {aca2d0304d8a1a8c6fa193149b5d6cc1}, keywords = {}, publisher = {Gesellschaft f{\"u}r Informatik, Bonn}, timestamp = {2024-09-10T15:15:57.000+0200}, title = {Extended Affinity Propagation clustering for Multi-source entity resolution}, year = 2021 }