Automated Development of Semantic Data Models Using Scientific Publications

The traditional methods for analyzing information in digital documents have evolved with the ever-increasing volume of data. Some challenges in analyzing scientific publications include the lack of a unified vocabulary and a defined context, different standards and formats in presenting information, various types of data, and diverse areas of knowledge. These challenges hinder detecting, understanding, comparing, sharing, and querying information rapidly. I design a dynamic conceptual data model with common elements in publications from any domain, such as context, metadata, and tables. To enhance the models, I use related definitions contained in ontologies and the Internet. Therefore, this dissertation generates semantically-enriched data models from digital publications based on the Semantic Web principles, which allow people and computers to work cooperatively. Finally, this work uses a vocabulary and ontologies to generate a structured characterization and organize the data models. This organization allows integration, sharing, management, and comparing and contrasting information from publications