Dissertation Defense: Jessica Cheng
Doctoral candidate Jessica Cheng will defend her doctoral dissertation, "Agreeing to Disagree: Applying a Logic-based Approach to Reconciling and Merging Multiple Taxonomies." Her committee includes Professor Bertram Ludäscher (chair); Professor Allen Renear; Assistant Professor Karen Wickett; and Nico Franz, professor in the School of Life Sciences at Arizona State University.
Abstract: Taxonomies are used to classify concepts into hierarchies via parent-child (is-a) relationships. They are used in many contexts, ranging from everyday sorting decisions, enterprise website infrastructures, to controlled vocabularies in information systems. The proliferation of taxonomies leads to interoperability challenges when one attempts to incorporate existing taxonomies with a newly created taxonomy, or to integrate multiple data in a digital library that were prepared by different taxonomies.
To address these interoperability challenges, methods to systematically align two taxonomies and merge them into a single merged view have been developed in extant literature. However, merging taxonomies into a unified representation may result in the loss of important information that was present in the original taxonomies. The goals of this dissertation are thus to align and merge taxonomies that: (1) preserve the information in both taxonomies in the merged solution(s); (2) provide multiple possible solutions.
Specifically, this dissertation explores the use of a logic-based approach to align taxonomies in which concepts in two taxonomies T1, T2 are inter-linked via a set of relations to yield merged solution(s). The merged solution(s) can be (1) a unique merged taxonomy T3 that preserves both T1 and T2’s information; (2) an inconsistent result that suggests the relations linking the taxonomies are contradictory; or (3) multiple merged solutions that present different possible ways (or possible worlds) in which T1 and T2 can be aligned. In this dissertation, Jessica applies this logic-based alignment approach to taxonomies in socio-geographic contexts, including the United States maps, country taxonomies, indigenous peoples' tribes, and historical sovereignties in biodiversity data. Through these use cases, it is demonstrated that the logic-based taxonomy alignment approach is feasible to reconcile conflicts in socio-geographic taxonomies.