Neural Network Algorithms for Ontology Informed Information Extraction
Author
Xu, DongfangIssue Date
2021Keywords
Biomedical Concept NormalizationInformation Extraction
Neural Network
Ontology
Time Normalization
Advisor
Bethard, Steven
Metadata
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The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Abstract
Ontology, as a formal and explicit specification of a shared conceptualization for a particular domain, is useful in information extraction. On the one hand, since information extraction is concerned with retrieving information for a particular domain, formally and explicitly specifying the concepts of that domain through an ontology defines the boundary of what information needs to be extracted. On the other hand, an ontology, typically consisting of classes (or concepts), attributes (or properties), and relationships (or relations among class members), contains the structured information that information extraction systems aim to extract. In this thesis, we are interested in how using an ontology can improve the information extraction process. We explore two research directions that both employ ontologies in the information extraction process, temporal normalization and biomedical concept normalization. In both research directions, we show that leveraging resources in ontologies helps to build high-performance information extraction systems, and presenting the extracted output using such ontologies makes the structured information concise and interchangeable.Type
textElectronic Dissertation
Degree Name
Ph.D.Degree Level
doctoralDegree Program
Graduate CollegeInformation