Description
Ontologies play an important role in storing and exchanging digitized data. As the need for semantic web information grows, organizations from around the globe has defined ontologies in different domains to better represent the data. But different organizations define ontologies of the same entity in their own way. Finding ontologies of the same entity in different fields and domains has become very important for unifying and improving interoperability of data between these multiple domains. Many different techniques have been used over the year, including human assisted, automated and hybrid. In recent years with the availability of many machine learning techniques, researchers are trying to apply these techniques to solve the ontology alignment problem across different domains. In this study I have looked into the use of different machine learning techniques such as Support Vector Machine, Stochastic Gradient Descent, Random Forest etc. for solving ontology alignment problem with some of the most commonly used datasets found from the famous Ontology Alignment Evaluation Initiative (OAEI). I have proposed a method OntoAlign which demonstrates the importance of using different types of similarity measures for feature extraction from ontology data in order to achieve better results for ontology alignment.
Details
Title
- Improving Ontology Alignment Using Machine Learning Techniques
Contributors
- Nasim, Tariq M (Author)
- Bansal, Srividya (Thesis advisor)
- Mehlhase, Alexandra (Committee member)
- Banerjee, Ayan (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2022
Subjects
Resource Type
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Note
- Partial requirement for: M.S., Arizona State University, 2022
- Field of study: Computer Science