| Date: | 2021, June 22 |
| Time: | 10:30 a. m. |
| Place: | Online |
| Author: | Glauer, Martin |
| Title: | Ontology Learning |
Advances in linked data enabled numerous interdisciplinary collaborations. The backbone of these data models is often built by ontologies. Yet, the manual ontology development process is often slow and labor-intensive. This raises the need for an automated way to generate ontologies. In this talk, we will look at some approaches in ontology learning, which are often based on statistical measures or linguistic analyses of large corpora of domain-specific texts. Additionally, we will discuss the influence these approaches have on the structure of the resulting ontology and compare it with what manual ontology development may yield.