 In the context of personal ontology construction, semantic distances between concepts need to reflect personal preferences. Based on that, this paper presents a supervised hierarchical clustering framework to incorporate personal preferences for distance metric learning in personal ontology construction. In this framework, periodic manual guidance provides training data for learning a distance metric and the learned metric is used during automatic activities to further construct the ontology. A detailed user study demonstrates that the approach is effective and accelerates the construction of personal ontologies.
