Abstract. Benchmarking approaches for ontology merging is challenging and has received little attention so far. A key problem is that there is in general no single best solution for a merge task and that merging may either be performed symmetrically or asymmetrically. As a first step to evaluate the quality of ontology merging solutions we propose the use of general metrics such as the relative coverage of the input ontologies, the compactness of the merge result as well as the degree of introduced redundancy. We use these metrics to evaluate three merge approaches for different merge scenarios. MotivationOntologies and taxonomies are increasingly used to semantically categorize or annotate information, e.g., for e-commerce or e-science. For example, product catalogs of online shops or comparison portals categorize products to help users and applications finding relevant information. Since many ontologies refer to the same domain and to the same objects, there is a growing need to integrate or merge such related ontologies. The goal is to create a merged ontology providing a unified view on two or more input ontologies.Ontology merging is a challenging problem especially for large and heterogeneous ontologies and require semi-automatic approaches to reduce the manual effort. Several such merge approaches have already been proposed, however their relative quality is largely unknown. One increasingly adopted and promising idea is to decompose the complex integration problem into match and merge subtasks and leverage the advances made for automatic ontology and schema matching [13] to solve the first subproblem. The merge subtask can then utilize a match mapping identifying corresponding concepts in the input ontologies that should be merged. Such a match-based merging is followed in [11] [7]) are most common and aim at completely integrating all input ontologies with the same priority. Asymmetric approaches, by contrast, take one of the input ontologies as the target and merge the other input ontologies into this target [11] [15] [6] thereby giving preference to the target ontology.Given the different merge approaches we see an increasing need to quantitatively evaluate their quality and performance. For the subproblem of ontology matching such evaluations are now quite common [1] [3] and there is also a benchmark for determining schema mappings [2]. Typically the quality of a match algorithm is determined by evaluating it on some match problems for which a manually defined perfect match result is provided for comparison. While a similar approach for evaluating merge approaches has been advocated for in [4] we argue that there is in general no single perfect merge
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. I. INTRODUCTIONOntologies 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
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