Bibliometry-Aware and Domain-Specific Features for Discovering Publication Hierarchically-Ordered Contexts and Scholarly-Communication Structures

Discovering publication hierarchically-ordered contexts is the main task in context-based searching paradigm. The proposed techniques to discover publication contexts relies on the availability of domain-specific inputs, namely a pre-specified ontology terms. A problem with this technique is that the needed domain-specific inputs may not be available in some scientific disciplines. In this paper, we propose utilizing a powerful input that is naturally available in any scientific discipline to discover the hierarchically-ordered contexts of it, namely paper citation and co-authorship graphs. More specifically, we propose a set of domain-specific bibliometry-aware features that are automatically computable instead of domain-specific inputs that need experts’ efforts to prepare. Another benefit behind considering bibliometric-features to adapt to the special characteristics of the literature environment being targeted, which in turn facilitates contexts membership decision making. One key advantage of our proposal is that it considers temporal changes of the targeted publication set.