Ontology Summarization via Machine Learning Techniques
Ontologies are developed for representing requirements and specifications that can help domain experts and engineers do various tasks in the real world. Usually, as the ontologies are quite large, it takes a long time for human users to understand the ontologies and it is also quite difficult for machines to do reasoning on such large ontologies. The goal is that we can explore different machine learning techniques to extract relevant parts of the ontology and reduce reasoning time.
The goal of this project is to find out a best strategy for extracting relevant knowledge from large ontologies, such as Snomed CT. With the help from the supervisor, you will
- read relevant papers about how to extract knowledge from ontology, such as ontology modularity and how to combine semantic technologies with machine learning techniques, such as ontology embedding;
- apply the knowledge that you learn from the course of "Semantic Technologies" in the real-world scenario;
- explore different knowledge extraction techniques and use reasoners on large scale ontologies;
- build a framework of knowledge extraction for ontologies.