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.
READ-IT members will take part in a workshop, ‘The Future and the Past of Reading. New Research Methods for new Perspectives’ at the Università degli Studi di Milano, Italy, on Thursday 18...
READ-IT members will take part in a workshop, ‘The Future and the Past of Reading. New Research Methods for new Perspectives’ at the Università degli Studi di Milano, Italy, on Thursday 18...
On 30 September 2021, The Open University’s funded period on the project READ-IT comes to an end. While the project team Dr. Shafquat Towheed (UK Lead), Dr. Alessio Antonini, Dr. Francesca Benatti,...
READ-IT members Dr. Alessio Antonini, Dr.Francesca Benatti and Prof. Corinna Norrick-Rühl have co-written a paper titled “Reading Popular Culture Offline and Online: Outlining a Comparative Study...
Last week saw the latest edition of the Semantics series of conferences, Semantics 2021, which took place in Amsterdam, Holland. Since 2005, the Semantics conferences provide the main European...
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SciRoc 2021 took place at the historic Palazzo Re Enzo, from Monday 6th – Friday 10th September 2021. 13 international teams joined the competition. Given the restrictions and uncertainty around...
Simone AngioniVisiting Student
Alessio AntoniniResearch Associate
Gianluca BardaroResearch Associate
Jason CarvalhoProject Officer - Data Hub Development
Agnese ChiattiPhD Research Student
Enrico DagaResearch Fellow
Alba Morales TiradoPhD Research Student
Enrico MottaProfessor of Knowledge Technologies
Francesco OsborneResearch Fellow
Angelo SalatinoResearch AssociateIn this video, the Health & Safety robot inspector under development at ISDS, which is called HanS, is navigating KMi searching for a set of relevant everyday objects. The robot’s location...
A video produced by the My New Term team describing the collaboration between My New Term and The Open University in the context of the CityLabs innovation...
An early demo of Hans our robot who is aware of our health and safety guidelines and is able to navigate the lab, checking that these are enforced. In this early demo Hans goes around KMi spotting...
Meloni, A., Angioni, S., Salatino, A.A., Osborne, F., Recupero, D. and Motta, E. (2021) AIDA-Bot: A Conversational Agent to ExploreScholarly Knowledge Graphs, The International Semantic Web Conference, Online
Daga, E., Asprino, L., Mulholland, P. and Gangemi, A. (2021) Facade-X: An Opinionated Approach to SPARQL Anything Volume 53: Further with Knowledge Graphs, eds. Mehwish Alam,Paul Groth,Victor de Boer,Tassilo Pellegrini,Harshvardhan J. Pandit, 53, pp. 58-73, IOS Press
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, IEEE Access, 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, International Journal on Digital Libraries, 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)
Scholarly Knowledge Mining
Digital Humanities
Data Science
Smart Cities and Robotics
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