Path Based Hierarchical Clustering on Knowledge Graphs
This addresses the need for automated reasoning methods in knowledge graphs, which are widely used for storing relational data, but the approach appears incremental as it builds upon earlier work.
The paper tackles the problem of automatically inducing a hierarchy of subject clusters from knowledge graphs, building on prior taxonomy induction work, and demonstrates its ability to produce a coherent cluster hierarchy on three real-world datasets.
Knowledge graphs have emerged as a widely adopted medium for storing relational data, making methods for automatically reasoning with them highly desirable. In this paper, we present a novel approach for inducing a hierarchy of subject clusters, building upon our earlier work done in taxonomy induction. Our method first constructs a tag hierarchy before assigning subjects to clusters on this hierarchy. We quantitatively demonstrate our method's ability to induce a coherent cluster hierarchy on three real-world datasets.