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In this paper we investigate the use of probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors provide a model to represent service descriptions of any type in vector form. With this conversion, heterogeneous service descriptions can be represented on the same homogeneous plane thus achieving interoperability between...
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