The Intelligent Systems and Data Science team carries out research in a variety of areas relevant to the development of user-centric, intelligent, data-intensive solutions, including Data Science, Semantic Web Technologies, Visual Analytics, Robotics, Large-Scale Data Infrastructures, Internet of Things, Machine Learning, and Human-Computer Interaction. Application domains include Smart Cities, Scholarly Data and Digital Humanities.

Our research is characterised by an emphasis on developing concrete solutions for concrete user audiences, while at the same time ensuring problems are approached by means of a broad socio-technological perspective. In addition, we also believe that most interesting problems can only be addressed through interdisciplinary approaches, hence our solutions tend to integrate different classes of computational methods, such as augmenting scalable data mining techniques with semantic technologies and background knowledge to improve both system performance and explainability.

Recent highlights comprise our work on the MK:Smart and MK:5G projects, which includes the development of the MK:Insight portal and the MK Data Hub infrastructure; our ongoing collaboration with Springer Nature on decision support tools for the academic publishing industry; our work on ‘robots in smart cities’ in the context of the SciRoc and Gatekeeper projects, and our collaborations with social scientists and musicologists to support and improve research methods in the Humanities.

In addition to our core R&D work, we are also closely engaged with the wider business community both in Milton Keynes and the wider SEMLEP region and indeed we consider innovation support a key aspect of our mission. In particular, we ran CityLabs, an innovative technology transfer programme funded by the European Regional Development Fund, which enabled SMEs in our region (South East Midlands) to take advantage of our expertise in Intelligent Systems and Data Science to develop innovative products and services for the digital economy.

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2021

Nayyeri, M., Cil, G., Vahdati, S., Osborne, F., Kravchenko, A., Angioni, S., Salatino, A.A., Recupero, D., Motta, E. and Lehmann, J. (2021) Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain, 9, pp. 116002-116014

Salatino, A.A., Osborne, F. and Motta, E. (2021) CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics, pp. (Early Access)

Larasati, R., De Liddo, A. and Motta, E. (2021) AI Healthcare System Interface: Explanation Design for Non-Expert User Trust, ACM IUI 2021. Workshop 7: Transparency and Explanations in Smart Systems - TExSS

Chiatti, A., Motta, E., Daga, E. and Bardaro, G. (2021) Fit to Measure: Reasoning about Sizes for Robust Object Recognition, Proceedings of the AAAI2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), International Virtual Event

Danilo, D., Osborne, F., Recupero, D., Buscaldi, D. and Motta, E. (2021) Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain, Future Generation Computer Systems, 116, pp. 253-264

Daga, E., Meroño-Peñuela, A. and Motta, E. (2021) Sequential Linked Data: the State of Affairs, pp. (In press)

Mulholland, P., Daga, E., Daquino, M., Díaz-Kommonen, L., Gangemi, A., Kulfik, T., Wecker, A., Maguire, M., Peroni, S. and Pescarin, S. (2021) Enabling multiple voices in the museum: Challenges and approaches, Digital Culture & Society, pp. (In Press)

Salatino, A.A., Mannocci, A. and Osborne, F. (2021) Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs Prediction Dynamics of Research Impact, eds. Yannis Manolopoulos,Thanasis Vergoulis, Springer (In press)

Nayyeri, M., Cil, G., Vahdati, S., Osborne, F., Rahman, M., Angioni, S., Salatino, A.A., Recupero, D., Vassilyeva, N., Motta, E. and Lehmann, J. (2021) Trans4E: Link Prediction on Scholarly Knowledge Graphs, Neurocomputing, pp. (Early Access)

Daga, E., Asprino, L., Damiano, R., Agudo, B., Gangemi, A., Kuflik, T., Lieto, A., Marras, A., Pandiani, D., Mulholland, P., Peroni, S., Pescarin, S. and Wecker, A. (2021) Integrating citizen experiences in cultural heritage archives: requirements, state of the art, and challenges, pp. (In Press)

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