Target-driven merging of taxonomies with Atom

• We propose and evaluate a new approach called Atom for mapping-based taxonomy merging.
• Atom is an asymmetric algorithm that merges a source taxonomy into the target taxonomy.
• We propose to restrict the semantic overlap in the merge result by giving preference to the target taxonomy.
• We use semantic match mappings for an improved merge result.

The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies. We propose a new taxonomy merging algorithm called Atom that, given as input two taxonomies and a match mapping between them, can generate an integrated taxonomy in a largely automatic manner. The approach is target-driven, i.e. we merge a source taxonomy into the target taxonomy and preserve the target ontology as much as possible. In contrast to previous approaches, Atom does not aim at fully preserving all input concepts and relationships but strives to reduce the semantic heterogeneity of the merge results for improved understandability. Atom can also exploit advanced match mappings containing is-a relationships in addition to equivalence relationships between concepts of the input taxonomies. We evaluate Atom for synthetic and real-world scenarios and compare it with a full merge solution.