An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse Datasets
This work addresses the challenge of deduplicating massive, multi-faceted datasets for companies like CareerBuilder, but it is incremental as it builds on existing blocking methods.
The paper tackles the problem of entity resolution on large, sparse datasets by proposing an ensemble blocking scheme that combines two different blocking techniques to improve coverage and efficiency, resulting in a more effective deduplication process for CareerBuilder's diverse data sources.
Entity Resolution, also called record linkage or deduplication, refers to the process of identifying and merging duplicate versions of the same entity into a unified representation. The standard practice is to use a Rule based or Machine Learning based model that compares entity pairs and assigns a score to represent the pairs' Match/Non-Match status. However, performing an exhaustive pair-wise comparison on all pairs of records leads to quadratic matcher complexity and hence a Blocking step is performed before the Matching to group similar entities into smaller blocks that the matcher can then examine exhaustively. Several blocking schemes have been developed to efficiently and effectively block the input dataset into manageable groups. At CareerBuilder (CB), we perform deduplication on massive datasets of people profiles collected from disparate sources with varying informational content. We observed that, employing a single blocking technique did not cover the base for all possible scenarios due to the multi-faceted nature of our data sources. In this paper, we describe our ensemble approach to blocking that combines two different blocking techniques to leverage their respective strengths.