Abstract

Entity Resolution (ER) is the problem of algorithmically matching records, mentions, or entries that refer to the same underlying real-world entity. Traditionally, the problem assumes (at most) two datasets, between which records need to be matched. There is considerably less research in ER when k > 2 datasets are involved. The evaluation of such multipartite ER (M-ER) is especially complex, since the usual ER metrics assume (whether implicitly or explicitly) k < 3. This paper takes the first step towards motivating a k-tuple approach for evaluating M-ER. Using standard algorithms and k-tuple versions of metrics like precision and recall, our preliminary results suggest a significant difference compared to aggregated pairwise evaluation, which would first decompose the M-ER problem into independent bipartite problems and then aggregate their metrics. Hence, M-ER may be more challenging and warrant more novel approaches than current decomposition-based pairwise approaches would suggest.

Metadata

publication

Proceedings of the AAAI Conference on Artificial Intelligence 38 (21), 23434 …, 2024

year

2024

publication date

2024/3/24

authors

Adin Aberbach, Mayank Kejriwal, Ke Shen

link

https://ojs.aaai.org/index.php/AAAI/article/view/30417

resource_link

https://ojs.aaai.org/index.php/AAAI/article/download/30417/32484

journal

Proceedings of the AAAI Conference on Artificial Intelligence

volume

38

issue

21

pages

23434-23435