Modular Intelligent Systems
The abundance of data and the ability to process it at a massive scale has transformed many areas of research in the natural sciences. These data-driven methods have recently begun to be adopted in other fields of research which traditionally have not relied on computational approaches, such as the social sciences and humanities. As we continue forward, we will likely see an increase in the spread of data-driven approaches in these fields as more and more data is “born digital”, coupled with mass digitisation projects that aim to transform the mountains of paper archives that still exist into more usable, machine-readable digital copies.
In order to analysis, store and generally manage massive textual data sets, a distributed intelligent system for large-scale text analysis was collaboratively developed. Intelligent systems for the annotation of media content are increasingly being used for the automation of parts of social science research. In this domain the problem of integrating various Artificial Intelligence (AI) algorithms into a single intelligent system arises spontaneously.
As part of our research group, we built a modular system by combining multiple AI modules into a flexible framework in which they can cooperate in complex tasks. Our system combines data gathering, machine translation, topic classification, extraction and annotation of entities and social networks, as well as many other tasks that have been perfected over the past years of AI research.
Using this modular infrastructure, we have been able to realise a series of scientific studies over a vast range of applications. The framework is flexible and allows the design and implementation of modular agents, where simple modules cooperate in the annotation of a large data set without central coordination.
For an example of an modular, intelligent system, see the Clickable news application (no longer updating) which constantly learns the news preferences of its users to recommend the most appealing news each day.