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An Introduction to Entity Resolution in Python

Motivating Clustering

Explore how clustering transforms entity resolution from pairwise matching to collective classification. Understand graph-based methods to resolve conflicts, improve prediction accuracy, and handle dependencies between record matches for practical applications.

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A typical entity resolution pipeline starts with preprocessing records r~1=C(r1),…,r~n=C(rn)\tilde{r}_1=C(r_1),\ldots,\tilde r_n=C(r_n) individually. Next comes pairwise feature engineering sij=F(r~i,r~j)s_{ij}=F(\tilde{r}_i,\tilde{r}_j), followed by pairwise matching cij=M(sij)c_{ij}=M(s_{ij}), where c=1c=1 represents a match and c=0c=0 ...