| As the volume of data on the Web or in databases increases, data integration is becoming more expensive and challenging than ever before. One of the challenges is entity resolution when integrating data from different sources. References with different representations but referring to the same underlying entity need to be resolved. And, references with similar descriptions but referring to different entities need to be distinguished from one another. Correctly de-duplicating and disambiguating these entities is an essential task in preparing high quality data. Traditional approaches mainly focus on the attribute similarity of references, but they do not always work for datasets with insufficient information. However, in relational datasets like social networks, references are always associated with one or more relationships and these relationships can provide additional information for identifying duplicates.;In this thesis, we solve the entity resolution problem by using relationships in the relational datasets. We implement a relational entity resolution algorithm to resolve entities based on an existing algorithm, greatly improving its efficiency and performance. Also, we generalize the single-type entity resolution algorithm to a multi-type entity resolution algorithm for applications that require to resolve multiple types of reference simultaneously and demonstrate its advantage over the single-type entity resolution algorithm. To improve the efficiency of the entity resolution process, we implement two blocking approaches to reduce the number of redundant comparisons performed by other methods. In addition, we implement a disk-based clustering algorithm that addresses the scalability problem, and apply it on a large academic social network dataset. |
