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In this study, we propose a hybrid knowledge-based framework for author name disambiguation. The developed approach helps incrementally identify authors of documents in data acquired from various sources. The nature of the problem calls for an orchestrated use of several methods; thus, the framework is composed of two levels. The first level contains a rule-based disambiguation algorithm. The second...
In this study, we propose the architecture of a content-based recommender system aimed at the selection of reviewers (experts) to evaluate research proposals or articles. We introduce a comprehensive algorithmic framework supported by various techniques of information retrieval. We propose a well-rounded methodology that explores concepts of data, information, knowledge, and relations between them...
The aim of this paper was to propose a classification system composed of monolingual classifiers and a multilingual decision module, for handling large numbers of multilingual documents. The system was compared with two monolingual classifiers, respectively for English and Polish, and with the maximum probability model. The tests were carried out over multilingual documents that contained components...
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