On Recommending Evolution Measures: A Human-Aware Approach
Kostas Stefanidis, Haridimos Kondylakis, Georgia Troullinou · 2017
As knowledge bases are constantly evolving, there is a clear need for monitoring and analyzing the changes that occur on them. Traditional approaches for studying the evolution of data focus on providing humans with deltas that include loads of information. In this work, we envision a processing model that recommends evolution measures taking into account particular challenges, such as relatedness, transparency, diversity, fairness and anonymity. We target at supporting humans with complementary measures that offer high-level overviews of the changes to help them understand how data of interest evolves.