Selecting candidate labels for hierarchical document clusters using association rules

One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for each of the obtained clusters. In many cases the labels have to be built using only the terms in the documents of the collection. This paper presents the SeCLAR (Selecting Candidate Labels using Association Rules) method, which explores the use of association rules for the selection of good candidates for labels of hierarchical document clusters. The candidates are processed by a classical method to generate the labels. The idea of the proposed method is to process each parent-child relationship of the nodes as an antecedent-consequent relationship of association rules. The experimental results show that the proposed method can improve the precision and recall of labels obtained by classical methods. © 2010 Springer-Verlag.

Improving hierarchical document cluster labels through candidate term selection

One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for each of the obtained clusters. In many cases the labels must be built using all the terms in the documents of the collection. This paper presents the SeCLAR method, which explores the use of association rules in the selection of good candidates for labels of hierarchical document clusters. The purpose of this method is to select a subset of terms by exploring the relationship among the terms of each document. Thus, these candidates can be processed by a classical method to generate the labels. An experimental study demonstrates the potential of the proposed approach to improve the precision and recall of labels obtained by classical methods only considering the terms which are potentially more discriminative. © 2012 - IOS Press and the authors. All rights reserved.

Challenges of Knowledge Representation in Contemporary Archival Science

Since its emergence as a discipline, in the nineteenth century (1889), the theory and practice of Archival Science have focused on the arrangement and description of archival materials as complementary and inseparable nuclear processes that aim to classify, to order, to describe and to give access to records. These processes have their specific goals sharing one in common: the representation of archival knowledge. In the late 1980 a paradigm shift was announced in Archival Science, especially after the appearance of the new forms of document production and information technologies. The discipline was then invited to rethink its theoretical and methodological bases founded in the nineteenth century so it could handle the contemporary archival knowledge production, organization and representation. In this sense, the present paper aims to discuss, under a theoretical perspective, the archival representation, more specifically the archival description facing these changes and proposals, in order to illustrate the challenges faced by Contemporary Archival Science in a new context of production, organization and representation of archival knowledge.