Hybrid AI: Integrating large scale data analysis with semantic technologies
Over the past 10-15 years we have witnessed a paradigm shift in computer science, brought about by the availability of large scale amounts of data. As a result of this paradigm shift new opportunities have arisen, allowing us to study the dynamics of a variety of phenomena, such as social networks, people’s mobility, shopping behaviours, etc.
To this purpose new machine learning and data mining solutions have been devised, which are able to effectively identify patterns in the data and support classification and intelligent decision making – e.g., targeted advertising for commercial or political purposes.
However, a drawback of these solutions is that they exhibit limited explanation capabilities, i.e., they can effectively identify patterns in the data but they cannot explain what these patterns actually mean, nor (usually) where the patterns come from.
To address these limitations we have developed an array of new solutions, which integrate large-scale data mining with semantic technologies and background knowledge to improve the ability of a system not only to process large scale amounts of data but also to extract meaning from them.
In the talk I will illustrate these ideas drawing on our research on scholarly analytics, which has produced innovative algorithms that assist users both in making sense of the dynamics of research communities and also in predicting future trends. In the talk I will also present the commercial spin-offs of this research, in particular illustrating how these methods have been customised to support business processes in Springer Nature.
Prof Enrico Motta has a Ph.D. in Artificial Intelligence from The Open University, where is currently a Professor in Knowledge Technologies. In the course of his academic career he has authored over 300 refereed publications and his h-index is 66, an impact indicator that puts him among the top computer scientists in Europe.
His research focuses on large scale data integration and analysis to support decision making in complex scenarios. Among his recent projects, he has led the MK:Smart initiative, a £17.2M project that tackled key barriers to economic growth in Milton Keynes through the deployment of innovative data-intensive solutions. He is also currently working on new solutions for scholarly analytics and in particular he is collaborating with Springer Nature to develop new tools that can improve both the quality and efficiency of editorial processes in the academic publishing industry.
In addition to his professorial position at The Open University, he is also Editor-in-Chief of the International Journal of Human-Computer Studies and over the years he has advised strategic research boards and governments in several countries, including UK, US, The Netherlands, Austria, Finland, and Estonia.