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Topimatch: Schema matching using descriptions

The problem of matching schemas is a tough, older problem, which is still current. In this report Schema Matching is part of the translation process that is needed when an e-commerce application uses web-services.
Finture an ASP (Application Service Provider) based application, developed by Topicus (an innovative IT service provider) uses the HDN (Mortgage Data Network) webservices to communicate with other financial institutions. These HDN messages are in another format (syntactically and semantically) than used by Finture. A translation between these formats is required, schema matching is part of this process. Currently this is done manually, which is time consuming, there is a need to automated this process.
Web-services are usually well documented, having descriptions on the element-level of the schema. The rationale is to use these descriptions (auxiliary information) as similarity clues. This leads to the following hypothesis: “Element-level descriptions can be used as similarity clues by a matcher, to increase the quality of the matches”
To test this thesis, a baseline comparison between a standard schema matching system and a system including a matcher that uses descriptions must be done. A literature study into the current state of the art regarding schema matching concludes that promising systems exist, but they are not available for testing. Therefore a new minimal functional schema system is designed and implemented, which is used for the baseline comparison.
The new matcher that uses descriptions as similarity clues, is named the Description Matcher. This new matcher is based on the Vector Space Model from the Information Retrieval (IR) research field. This model uses the descriptions as queries and documents.
The set of documents can be searched with a query. If a query (description linked to a source schema element) yields as a result a document (description linked to a target schema element), there is a similarity between the source and target scheme elements.
In the Vector Space Model, query and documents are expressed as vectors in a multidimensional space were each term has its own axis. Similarity is then expressed as the angle between the query and document vector, the smaller the angle the more similar they are. Another important aspect is Term Weighting which is used to assign a weight of relevance to a term. This to promote unique terms or to demote common terms (e.g., the, and, if, or).
This new matcher is evaluated using well known quality measures: Precision, Recall and Overall (a combined form of Precision and Recall). The results of the test schemas that were used show an increase in these measures, meaning the hypothesis is correct.
The remaining question is, how significant this increase is? More research is needed to answer this question, but nevertheless the usage of descriptions as a similarity measure is promising.