Schema Evolution publication categorizer

. We view match as an operator that takes two graph-like structures (e.g., XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Se- mantic schema matching is based on the two ideas: (i) we discover mappings by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codified in the elements and the structures of schemas.

Matching hierarchical structures, like taxonomies or web di-
rectories, is the premise for enabling interoperability among heteroge-
nous 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 per-

. Schema and ontology matching is a critical problem in many application domains, such as semantic web, schema/ontology integration, data warehouses, e-commerce, etc. Many different matching solutions have been proposed so far. In this paper we present a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching. Some innovations are in introducing new criteria which are based on (i) general properties of matching techniques, (ii) interpretation of input information, and (iii) the kind of input information.