Abstract-The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies to provide a unified view on them. We demonstrate a new automatic approach to merge large taxonomies such as product catalogs or web directories. Our approach is based on an equivalence matching between a source and target taxonomy to merge them. It is target-driven, i.e. it preserves the structure of the target taxonomy as much as possible. Further, we show how the approach can utilize additional relationships between source and target concepts to semantically improve the merge result. Ontologies and taxonomies are increasingly used to semantically classify or annotate information in a lot of different application contexts. For example, in life sciences, ontologies are used to describe components and functions of organisms or objects such as genes or proteins; on the web, product catalogs of online shops, comparison portals or web directories use taxonomies to classify products or websites and to help users and applications finding relevant information. Many ontologies have been designed for the same domain in an independent way and there is a growing need to integrate or merge them with the goal to create a single ontology providing a unified view on the input ontologies while maintaining all information coming from them. The ontology integration problem was investigated during the last years, but it is still a challenge if one wants to perform the integration in a largely automatic way. The related research problem of schema integration has been studied thoroughly for a long time [2] but most earlier approaches suffered from trying to solve the complex problems of matching and merging in a single approach. More recent work on schema integration builds on the research results on semiautomatic schema matching [9] and separate matching from merging. Hence, several algorithms have been proposed to merge schemas based on a pre-determined match mapping [3] , [11], [6], [8] . Despite this simplification, several of these merge approaches are still not fully automatic but depend on manual intervention. Previous approaches on ontology merging [5] , [4], [12] are also user-controlled and do not utilize the separation of matching and merging. While usercontrolled approaches provide flexibility for determining the merge result, they require the involvement of expensive data integration experts and introduce a substantial manual effort especially for large ontologies. The AUTOMATIC TARGET-DRIVEN ONTOLOGY MERGING (ATOM) system is an attempt at overcoming these limitations. It implements a new approach for taxonomy merging which
ATOM takes two input ontologies and makes an equivalence matching between them, which generates an integrated ontology in a fully automatic manner REF .
ATOM: Automatic target-driven ontology merging
2011 IEEE 27th International Conference on Data Engineering
2011 IEEE 27th International Conference on Data Engineering
Computer Science