Tools and applications

d-index: a way to evaluate sientific profiles

The evaluation of the career of a researcher and its impact on the research community has been deeply studied in literature through the definition of several measures, first among all the h-index and its variations. Although these measures represent valuable tools for analyzing researchers' outputs, they usually assume the co-authorship to be a proportional collaboration between the parts, missing out their relationships and the relative scientific influences. This web application is based on d-index, a novel measure that estimates the dependency degree between authors according to their research environment and along their entire scientific publication history. It proposes a number of visualization tools for analyzing and comparing the careers of all the scientists in the DBLP bibliographic database.

d-index is available at: http://d-index.di.unito.it.

CoSeNa: A context-aware navigation system

The CoSeNa System propose an innovative approach to document exploration and retrieval. It allows user to explore text collections leveraging a novel keywords-by-concepts (KbC) graph model, which supports navigation using domain-specific concepts as well as keywords that are characterizing the text corpus. The KbC graph is a weighted graph, created by tightly integrating keywords extracted from documents and concepts obtained from domain taxonomies and supports contextually informed access to these documents.

The behavior of the CoSeNa System is described in: M. Cataldi, C. Schifanella, K. S. Candan, M. L. Sapino, L. Di Caro. "CoSeNa: a context-based search and navigation system". In MEDES09, International ACM Conference on Management of Emergent Digital EcoSystems, Lyon, 2009.

A demostration version of the software can be downloaded [here].

A metadata-informed co-clustering framework

In traditional co-clustering, the only basis for the clustering task is a given relationship matrix, describing the strengths of the relationships between pairs of elements in the different domains. In many real life applications background knowledge or metadata about one or more of the two input domain dimensions may be available. The proposed framework (developed in Java), proposes three different algorithms to metadata-informed coclustering, named metadata-driven, metadata-constrained and metadata-injected.

Details about the proposed techniques can be found in: C. Schifanella, M. L. Sapino, K. S. Candan "On Context-Aware Co-Clustering with Metadata Support". Journal of Intelligent Information Systems, 2011. Springer. To appear.