Distributed resource discovery through exchanges of examples and classifiers

Abstract

Distributed resource discovery is an essential step for information retrieval and providing information services. This step is usually used for determining the location of an information/data repository that has relevant information/data. The most fundamental challenge is the potential lack of semantic interoperability among these repositories. In this paper, we proposed an algorithm to enable distributed resource discovery. In the proposed method, the distributed repositories achieve pair wise semantic interoperability through the exchange of both examples (either in the form of raw data or through a set of descriptors) and the classifiers (which have been trained on the raw data or the descriptors). For each repository, the local classifier is used to classify the examples sent by the remote repository, and the classifier from the remote repository is used to classify the examples from the local repository. The correspondence of the class labels from two repositories can then be established by examining the classification results.