This paper reports the runner-up solution to the ACM SIGMOD 2020 programming contest, whose target was to identify the specifications (i.e., records) collected across 24 e-commerce data sources that refer to the same real-world entities. First, we investigate the machine learning (ML) approach, but surprisingly find that existing state-of-the-art ML-based methods fall short in such a context-not reaching 0.49 F-score. Then, we propose an efficient solution that exploits annotated lists and regular expressions generated by humans that reaches a 0.99 F-score. In our experience, our approach was not more expensive than the dataset labeling of match/non-match pairs required by ML-based methods, in terms of human efforts.
Entity resolution on camera records without machine learning / Zecchini, L.; Simonini, G.; Bergamaschi, S.. - 2726:(2020). (Intervento presentato al convegno 2nd International Workshop on Challenges and Experiences from Data Integration to Knowledge Graphs, DI2KG 2020 tenutosi a jpn nel 2020).