Recommendations for qualitative ontology matching evaluations

The final component is Best Match Pairs Selection and Interpretation where bi-objective optimization problem is solved using Pareto optimization technique wherein both

In this section, we detail the overall process that ServOMap follows for computing similarity between entities of two given ontologies and more generally two

However, in contrast to approaches using co-occurence analysis on Wikipedia [18] or Google Distance [6], which require a quadratic number of search requests, our approach only issues

Evaluating different matchers on these generated tests, we have observed that (a) the difficulties en- countered by a matcher at a test are preserved across the seed ontology,

The process is split in two different phases: (i) in the first one, we created the index containing information about the target ontology represented in a structured way; while, (ii)

In recent years, ontology matching systems have had a growing interest in terms of reasoning capabilities, and we propose that a combination of these strategies with pattern-

If we use only the structural step, without using the semantic step before, with all possible correspondences, not only the temporarily suspended corre- spondences, we would have

We developed two dierent approa- ches and a combined approach that are based on a large biomedical ontology, its alignment to the competence eld ontology designed by us, and