Wir machen unsere Kompetenzen und Innovationen
nach Außen hin sichtbar.

The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for known relation types and stores the centroids in a central Knowledge Base (KB). Comparing verb vectors extracted from unknown relations with the stored centroids yields link-type suggestions. Domain experts evaluate these suggestions, refining the KB and constantly improving the components accuracy. Using four sample ontologies on ’energy sources’, this paper demonstrates how link-type suggestion aids the ontology design process. It also provides a statistical analysis on the accuracy and average ranking performance of Batch Learning (BL) vs. Online Learning (OL).