Date of Award
1-30-2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Information Science
First Advisor
John Talburt
Abstract
The main objective of Entity resolution (ER) is to find duplicate records within the same data table from the same source or different data tables from various sources. A traditional pair-wise supervised entity resolution matching depends on pre-built rules for finding matched records. On the other hand, unsupervised or semisupervised also relies on pair-wise matching. In the maximum case, group membership is left behind for consideration. In this dissertation, I have discussed the design, implementation and evaluation of a graph-based entity resolution for group membership to enhance the pair-wise matching ER system. I have designed and implemented a pipeline for blocking, then clustering using Datawashing machine for a dataset and used the graph technique to find matching based on group membership. I have implemented this technique on synthetic data where the data set is first run through a pair-wise matcher, namely OYSTER. After that, Graph-based Group membership pipeline was used to enhance that pair-wise result, where we observed significant enhancement for the True Positive linkage.
Recommended Citation
Siddique, Md Abdus Salam, "The Application of Graph Technology for Improving Entity Resolution Results in the Context of Group Membership" (2023). Theses and Dissertations. 1106.
https://research.ualr.edu/etd/1106
