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. Comparing verb-vectors extracted from unknown
relations with the stored centroids yields link type suggestions.
Domain experts evaluate these suggestions, refining the knowledge
base and constantly improving the component's accuracy. A final evaluation
provides a detailed statistical analysis of the introduced approach.
Keywords: ontology learning, ontology extension, link type detection, non-hierarchical relations, non-taxonomic relations, vector space model