Title: Ontology Enrichment Based on Unstructured Text Data Author: Ivana Lukšová Department: Department of Software Engineering Supervisor: Mgr. Martin Nečaský, Ph.D., Department of Software Engi- neering Abstract: Semantic annotation, attaching semantic information to text data, is a fundamental task in the knowledge extraction. Several ontology-based semantic annotation platforms have been proposed in recent years. However, the process of automated ontology engineering is still a challenging problem. In this paper, a new semi-automatic method for ontology enrichment based on unstructured text is presented to facilitate this process. NLP and ma- chined learning methods are employed to extract new ontological elements, such as concepts and relations, from text. Our method achieves F-measure up to 71% for concepts extraction and up to 68% for relations extraction. Keywords: ontology, machine learning, knowledge extraction 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:324590 |
| Date | January 2013 |
| Creators | Lukšová, Ivana |
| Contributors | Nečaský, Martin, Kozák, Jakub |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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