On the Complexity of Learning Description Logic Ontologies
This work provides a theoretical framework for researchers in ontology learning, but it is incremental as it synthesizes and formalizes existing literature.
The paper formalizes computational learning models for ontology learning and reviews existing complexity results and approaches for learning lightweight description logic ontologies, without presenting new experimental results or numbers.
Ontologies are a popular way of representing domain knowledge, in particular, knowledge in domains related to life sciences. (Semi-)automating the process of building an ontology has attracted researchers from different communities into a field called "Ontology Learning". We provide a formal specification of the exact and the probably approximately correct learning models from computational learning theory. Then, we recall from the literature complexity results for learning lightweight description logic (DL) ontologies in these models. Finally, we highlight other approaches proposed in the literature for learning DL ontologies.