Full metadata
Title
Improving Ontology Alignment Using Machine Learning Techniques
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.
Date Created
2022
Contributors
- Nasim, Tariq M (Author)
- Bansal, Srividya (Thesis advisor)
- Mehlhase, Alexandra (Committee member)
- Banerjee, Ayan (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
81 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.171617
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2022
Field of study: Computer Science
System Created
- 2022-12-20 06:17:32
System Modified
- 2022-12-20 06:17:32
- 1 year 11 months ago
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