Previously funded by the NUCATS internal grant.
The C-IKNOW VIVO Recommender is the SONIC laboratory’s recommender system for collaborations in scientific research. The C-IKNOW VIVO Recommender embraces the Multi-Theoretical, Multi-Level (MTML) framework of social drivers to model the motivations of the seeker of a recommendation. The C-IKNOW VIVO Recommender integrates social network analysis to making recommendations. The C-IKNOW VIVO Recommender utilizes Linked Open Data (LOD) as a data representation, the Apache Jena Java framework for building Semantic Web applications, and the Java Universal Network/Graph Framework (JUNG) for network analysis.
The C-IKNOW VIVO Recommender, operating in the domain of recommending collaborations among the health sciences faculty of our collaborator the University of Florida, is available online.
The C-IKNOW Semantic Recommender is a collaboration between Mike Conlon and his team at the University of Florida, David Eichmann and his team at the University of Iowa, Maryam Fazel-Zarandi, PhD candidate at the Department of Computer Science, University of Toronto, and Noshir Contractor, Anup Sawant, Yun Huang, and Hugh Devlin of the SONIC lab.