Semantic heterogeneity can be defined as the use of different documentation languages or ontologies for different document sets in digital libraries or other integrated document collections. Automatic translation between terms of different ontologies (“ontology switching”) is one approach to solve this problem. The underlying statistical methods based on corpus analysis allow the automatic identification of semantic relations (or “cross-concordances”) between terms from different ontologies. These methods require parallel corpora with documents that are indexed with terms from different ontologies. For the social sciences suitable corpora are very rare. Therefore, special modifications of the ontology switching approach help to deal with this lack. One alteration is simulating a parallel corpus for un-indexed document collections using machine learning and linguistic methods. Another change is translating not between two ontologies but between an ontology and suitable free text terms.

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