Multipartite Entity Resolution: Motivating a K-Tuple Perspective (Student 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.

Authors

• Adin Aberbach Information Sciences Institute, University of Southern California
• Mayank Kejriwal Information Sciences Institute, University of Southern California
• Ke Shen Information Sciences Institute, University of Southern California

Keywords

• AI And The Web
• DMKM: Knowledge Acquisition From The Web
• Information Extraction
• Knowledge Discovery
• Natural Langauge Processing