The definitive classification of scientific journals depends on their aims and scopes details. In this paper, we present an approach to facilitate the journals classification of the DBLP datasets. For the analysis, the DBLP data sets were... more
The definitive classification of scientific journals depends on their aims and scopes details. In this paper, we present an approach to facilitate the journals classification of the DBLP datasets. For the analysis, the DBLP data sets were preprocessed by assigning each journal attributes defined by its topics and then the theory of formal concept analysis is introduced. It is subsequently shown how this theory can be applied to analyze the relations between journals and the extracted topics from their aims and scopes. The result is a concept lattice that contains information on journal-topic relational context depending on how they are associated. It is shown how this approach can be used to facilitate the classifications of scientific journals.
Given a finite set C:= C 1, ¼, C n C:={C_1, ..., C_n\} of description logic concepts, we are interested in computing the subsumption hierarchy of all least common subsumers of subsets of CC as well as the hierarchy of all conjunctions of... more
Given a finite set C:= C 1, ¼, C n C:={C_1, ..., C_n\} of description logic concepts, we are interested in computing the subsumption hierarchy of all least common subsumers of subsets of CC as well as the hierarchy of all conjunctions of subsets of C C. These hierarchies can be used to support the bottom-up construction of description logic knowledge bases. The point is to compute the first hierarchy without having to compute the least common subsumer for all subsets of CC, and the second hierarchy without having to ...
In this paper we describe the application of Formal Concept Analysis (FCA) for analysis of data tables with different types of attributes. FCA represents one of the conceptual data mining methods. The main limitation of FCA in classical... more
In this paper we describe the application of Formal Concept Analysis (FCA) for analysis of data tables with different types of attributes. FCA represents one of the conceptual data mining methods. The main limitation of FCA in classical case is the exclusive usage of binary attributes. More complex attributes then should be converted into binary tables. In our approach, called Generalized One-Sided Concept Lattices, we provide a method which deal with different types of attributes (e.g., ordinal, nominal, etc.) within one data table. Therefore, this method allows to create same FCA-based output in form of concept lattice with the precise many-valued attributes and the same interpretation of concept hierarchy as in the classical FCA, without the need for speci c uni ed preprocessing of attribute values.
The definitive classification of scientific journals depends on their aims and scopes details. In this paper, we present an approach to facilitate the journals classification of the DBLP datasets. For the analysis, the DBLP data sets were... more
The definitive classification of scientific journals depends on their aims and scopes details. In this paper, we present an approach to facilitate the journals classification of the DBLP datasets. For the analysis, the DBLP data sets were preprocessed by assigning each journal attributes defined by its topics and then the theory of formal concept analysis is introduced. It is subsequently shown how this theory can be applied to analyze the relations between journals and the extracted topics from their aims and scopes. The result is a concept lattice that contains information on journal-topic relational context depending on how they are associated. It is shown how this approach can be used to facilitate the classifications of scientific journals.
