In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. We refer to this task as "knowledge translation" (KT). Unlike data translation and transfer learning, KT does not require any data from the source or target schema. We adopt a probabilistic approach to KT by representing the knowledge in the source schema, the mapping between the source and target schemas, and the resulting knowledge in the target schema all as probability distributions, specially using Markov random fields and Markov logic networks. Given the source knowledge and mappings, we use standard learning and inference algorithms for probabilistic graphical models to find an explicit probability distribution in the target schema that minimizes the Kullback-Leibler divergence from the implicit distribution. This gives us a compact probabilistic model that represents knowledge from the source schema as well as possible, respecting the uncertainty in both the source knowledge and the mapping. In experiments on both propositional and relational domains, we find that the knowledge obtained by KT is comparable to other approaches that require data, demonstrating that knowledge can be reused without data.
Heterogeneous DDM @cite_1 also handles the semantic heterogeneity between the global and local schemas, in particular, those containing attributes with different granularities called Attribute Value Taxonomy (AVT). Heterogeneous DDM requires local data resources and their mappings to the global schema to translate the statistics of queries. However, KT does not require data or statistics from either the source or the target. Instead, KT uses mappings to translate the generated mined knowledge from the source directly.
@cite_1
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Access to large numbers of data sources introduces new problems for users of heterogeneous distributed databases. End users and application programmers must deal with unavailable data sources. Database administrators must deal with incorporating new sources into the model. Database implementers must deal with the translation of queries between query languages and schemas. The Distributed Information Search COmponent (Disco) addresses these problems. Query processing semantics are developed to process queries over data sources which do not return answers. Data modeling techniques manage connections to data sources. The component interface to data sources flexibly handles different query languages and translates queries. This paper describes (a) the distributed mediator architecture of Disco, (b) its query processing semantics, (C) the data model and its modeling of data source connections, and (d) the interface to underlying data sources.