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Deriving ``Sub-source'' Similarities from Heterogeneous, Semi-structured Information Sources


Domenico Rosaci, Giorgio Terracina, and Domenico Ursino

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Abstract

In this paper we propose a semi-automatic technique for deriving the similarity degree between two portions of heterogeneous, semi-structured information sources (hereafter, sub-sources). The proposed technique consists of two phases: the first one selects the most promising pairs of sub-sources, whereas the second one computes the similarity degree relative to each promising pair. In addition, we show that the detection of sub-source similarities is a special case (and a very interesting one, for semi-structured information sources) of the more general problem of Scheme Match. Finally we discuss some possible applications which can benefit of derived sub-source similarities. A real example case is presented for better clarifying the proposed technique.


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