Extracting academic data and linked data anonymization
Data is becoming more valuable each day as more diverse and rich
data sources become available, allowing us to discover knowledge
on unprecedented ways.
IST uses FénixEdu information system for managing most of internal
data. The system contains data about students, teachers, employees,
courses, and all major aspects of IST as an organization. Such data
may be useful for both external agents and, more importantly, for IST
itself to study our academic environment. Data may be used as input
for state-of-art IR and KD technologies to extract newer and deeper
knowledge about academic agents allowing to solve problems on and to
understand better our community.
Releasing this kind of data publicly comprises an additional
step in what concerns privacy preserving of referred individuals and,
as has been shown, simple de-identification may not be enough to achieve
such goal. On the other hand we must deal with both internal and
external data, on top of an evolving environment, where linked data
based approaches can definitely help us to deal with such complexity.
In this talk we will discuss a solution for exposing, sharing, and
connecting data, information, and knowledge available on IST information
system, taking into consideration privacy and anonymity issues.