Mining EL Bases with Adaptable Role Depth

Description Logic (DL) knowledge bases are one of the most prominent ways to formalise and share knowledge without ambiguity. They have been particularly successful in fields rich in terminological knowledge such as Biology, Medicine, and Manufacturing. Although the tools supporting ontology creation and maintenance have evolved over the years, building ontologies is still a demanding task. In particular, it involves not only ontology engineers, but domain experts as well. Moreover, the process of modelling knowledge, even with modern methodologies remains time-consuming. In some cases, it is possible to build an ontology from a structure (e.g. a database or knowledge graph) automatically. By adapting notions from Formal Concept Analysis to DLs one extract rules in the form of concept inclusions. In DLs, these concept inclusions can involve arbitrarily large expressions via nesting. Thus, it is not clear whether a finite base exists and, if so, how large concept expressions may need to be. We first revisit results in the literature for mining ontologies from finite interpretations in the description logic EL. Those mainly focus on finding a finite base while fixing the role depth but potentially losing some concept inclusions (with larger concepts) that hold in the interpretation. Then, we present a new strategy for mining EL bases that is adaptable in the sense that it can bound the role depth of concepts depending on the local structure of the interpretation without losing EL concept inclusions that hold in the interpretation.