Publication Date
2024
Document Type
Thesis
Committee Members
Cogan Shimizu, Ph.D. (Advisor); Wen Zhang, Ph.D. (Committee Member); Lingwei Chen, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
The effectiveness of a deployed knowledge graph is commonly evaluated with defined use-cases from domain experts. This poses challenges during the development cycle in determining how to represent data. Developers of a knowledge graph can optionally include semantics into a knowledge graph by abstracting the data representation in such a way that mirrors information as it exists in the real world. Consequently, the abstraction is represented by additional layers, resulting in performant differences in knowledge graph embedding; such as, the embedded model's ability to infer facts between entities through link predictions. This thesis presents a comprehensive analysis of the performance impact observed across a range of knowledge graph embedding models trained on FB15k-237, a widely recognized benchmark dataset for knowledge graph completion. Additionally, the experiment is performed with augmented versions of FB15k-237, serving to introduce semantics into the knowledge graph.
Page Count
60
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2024
Copyright
Copyright 2024, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0009-0004-1114-8702
