Schema Evolution publication categorizer

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.

Recently, schema mapping has found considerable interest in both
research and practice. Determining matching components of database or XML
schemas is needed in many applications, e.g. for e-business and data integra-
tion. In this paper a complete generic solution of the schema mapping problem
is presented. A hybrid semantic schema mapping algorithm which semi-
automatically finds mappings between two data representation schemas is in-
troduced. The algorithm finds mappings based on the hierarchical organization

This paper summarizes the OAEI Contest 2006 results for the matching tool
COMA++. The study shows that a generic schema matching system can also
effectively solve complex ontology matching tasks.

Schema matching aims at identifying semantic correspondences between elements of two schemas, e.g., database schemas, ontologies, and XML message formats. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. In today's systems, schema matching is manual; a time-consuming, tedious, and error-prone process, which becomes increasingly impractical with a higher number of schemas and data sources to be dealt with.