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Data-Mining: Assoziations-Algorithmen und die Auswirkungen auf generierte Regeln im Bereich der Biodiversität- Disambiguation of Ontological Concepts for Semantic Dataset Annotation
- Exploratory Semantic Dataset Search (taken)
- Reproducibility of Machine Learning Experiments given the provenance data (taken)
- Tracking Provenance in Machine Learning Scripts (taken)
Disambiguation of Ontological Concepts for Semantic Dataset Annotation
| Title: | Disambiguation of Ontological Concepts for Semantic Dataset Annotation |
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| Supervisor(s): | Felicitas Löffler, Prof. Dr. Birgitta König-Ries |
| School: | Friedrich Schiller University Jena |
| Thesis Type: | Master |
| Abstract: | Semantic Annotations are additional information on textual or structrued data that link terms to ontological concepts. This helps to incorporate additional information into the textual context. This text mining technique is getting increasing attention since it is required in text mining applications, semantic search or question answering systems. A main issue in creating semantic annotations is disambiguation, which means to define a clear context for a term or phrase. For instance, a Jaguar could be a car or an animal. Depending on the surrounding terms, a system could try to disambiguate the context and could select the correct ontology or vocabulary. The main task of the thesis is to develop a ranking function for annotation concept candidates based on the given context. The outcome should be a ranked list of suitable ontological concepts for a given term or phrase. The proposed solution is supposed to be evaluated against a manually annotated corpus of datasets. Datasets and ontologies will be provided by the supervisor. |