Semantic matching of schemas in heterogeneous data sharing systems is time consuming and error prone. Existing mapping tools employ semi-automatic techniques for mapping two schemas at a time. In a large-scale scenario, where data sharing involves a large number of data sources, such techniques are not suitable. We present a new robust automatic method which discovers semantic schema matches in a large set of XML schemas, incrementally creates an integrated schema encompassing all schema trees, and defines mappings from the contributing schemas to the integrated schema. Our method, PORSCHE (Performance ORiented SCHEma mediation), utilises a holistic approach which first clusters the nodes based on linguistic label similarity. Then it applies a tree mining technique using node ranks calculated during depth-first traversal. This minimises the target node search space and improves performance, which makes the technique suitable for large scale data sharing. The PORSCHE framework is hybrid in nature and flexible enough to incorporate more matching techniques or algorithms. We report on experiments with up to 80 schemas containing 83,770 nodes, with our prototype implementation taking 587 seconds on average to match and merge them, resulting in an integrated schema and returning mappings from all input schemas to the integrated schema. The quality of matching in PORSCHE is shown using precision, recall and F-measure on randomly selected pairs of schemas from the same domain. We also discuss the integrity of the mediated schema in the light of completeness and minimality measures.