In this work we present SIFT, a 3-step algorithm for the analysis of the structural information represented by means of a taxonomy. The major advantage of this algorithm is the capability to leverage the information inherent to the hierarchical structures of taxonomies to infer correspondences which can allow to merge them in a later step. This method is particular relevant in scenarios where taxonomy alignment techniques exploiting textual information from taxonomy nodes cannot operate successfully.
Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems. This paper deals with taxonomy alignment. It presents structural techniques of an alignment method suitable with a dissymmetry in the structure of the mapped taxonomies. The aim is to allow a uniform access to documents belonging to a same application domain, assuming retrieval of documents is supported by taxonomies. TaxoMap is an alignment tool which aims to discover rich correspondences between concepts (equivalence relations (isEq), subsumption relations (isA) and their inverse (isMoreGnl) or proximity relations (isClose)). It performs an oriented alignment (from a source to a target ontology) and takes into account labels and sub-class descriptions. This new implementation of TaxoMap uses a pattern-based approach implemented in the TaxoMap Framework helping an engineer to refine mappings to take into account specific conventions used in ontologies. Matching hierarchical structures, like taxonomies or web directories, is the premise for enabling interoperability among heterogenous data organizations. While the number of new matching solutions is increasing the evaluation issue is still open. This work addresses the problem of comparison for pairwise matching solutions. A methodology is proposed to overcome the issue of scalability. A large scale dataset is developed based on real world case study namely, the web directories of Google, Looksmart and Yahoo!. Finally, an empirical evaluation is performed which compares the most representative solutions for taxonomy matching. We argue that the proposed dataset can play a key role in supporting the empirical analysis for the research effort in the area of taxonomy matching.