Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation

TitleInteractive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation
Publication TypeJournal Articles
Year of Publication2008
AuthorsKang H, Getoor L, Shneiderman B, Bilgic M, Licamele L
JournalIEEE Transactions on Visualization and Computer Graphics
Pagination999 - 1014
Date Published2008/10//Sept
ISBN Number1077-2626
Keywordsalgorithms, Computer Graphics, D-Dupe, data visualisation, database management systems, Databases, Factual, graphical user interface, Graphical user interfaces, human-centered computing, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Information Visualization, interactive entity resolution, relational context visualization, Relational databases, relational entity resolution algorithm, User interfaces, user-centered design, User-Computer Interface, visual analytic tool

Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.