In order to address such scenarios, we are researching a novel set of techniques to develop an end-to-end framework to generate insights from unstructured data and to provide a means for knowledge-driven decision making for a multitude of industries. To support the framework, we are investigating means to
- combine natural-language processing techniques with Big Data principles to conduct efficient text analytics and machine-learning techniques for topic modelling to automatic term correlation
- interpret and represent the obtained information as knowledge in an efficient manner by focusing on data semantics 
- query-answer knowledge to support decision making using the latest thought processes in cognitive computing, especially using pragmatic models of communication as illustrated by this data and schema-aware query reformulation pyramid shown here [2, 6].
In the general case, our state-of-the-art algorithms to reformulate queries over large knowledge graphs have been shown to require only half the reformulations needed by the current state-of-the-art for similar results.
The components of this framework have been applied to a number of proof-of-concept implementations from engineering (for example, to provide insights about the thermal properties of the internal combustion engine by analysing patents) to life sciences (for example, to obtain information about biofilms from open-access journals available from the European PMC).
An emerging interest to the group going forward is to look at means to infer high-level semantics about structured data and correlate them with the insight gleaned from unstructured data to start modelling the uncertainty in knowledge so that human-like reasoning can be supported by our framework.
 E. Bertino, G., de Mel, A. Russo, S., Calo, D. Verma,
“Community-based Self Generation of Policies and Processes for Assets: Concepts and Research Directions,”
In Proc. IEEE Big Data workshop on Policy-based Autonomic Data Governance, 2017.
 A. Viswanathan, J.R. Michaelis, T. Cassidy, G. de Mel, J. Hendler,
“In-context query reformulation for failing SPARQL queries,”
In Proc. Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, International Society for Optics and Photonics, Vol. 10190, p. 101900M, 2017.
 E. Goynugur, G. de Mel, M. Sensoy, K. Talamadupula, S. Calo,
“A Knowledge Driven Policy Framework for Internet of Things,”
In Proc. 9th International Conference on Agents and Artificial Intelligence, 2017.
 E. Göynügür, S. Bernardini, G. de Mel, K. Talamadupula, M. Şensoy,
“Policy Conflict Resolution in IoT via Planning,”
In Proc. Canadian Conference on Artificial Intelligence, LNCS Vol. 10233, Springer, pp. 169-175, 2017.
 M. Şensoy, L. Kaplan, G. de Mel,
“Semantic reasoning with uncertain information from unreliable sources,”
In Proc. International Conference on Principles and Practice of Multi-Agent Systems, LNCS Vol. 9862, Springer, pp. 92-109, 2016.
 A. Viswanathan, G. de Mel, J.A. Hendler,
“Pragmatics and Discourse in Knowledge Graphs,” 2015.
 B. Donohue, D. Kutach, A. Bhattal, D. Braines, G. de Mel, R. Ganger, T. Pham, R. Rudnicki, B. Smith,
“Controlled and Uncontrolled English for Ontology Editing,”
In Proc. CEUR Workshop STIDS, Vol. 1523, pp. 74-81, 2015.
 A. Preece, D. Pizzocaro, D. Braines, D. Mott, G. de Mel, T. Pham,
“Integrating hard and soft information sources for D2D using controlled natural language,”
In Proc. 15th IEEE International Conference on Information Fusion (FUSION), pp. 1330-1337, 2012.
 M. Gomez, A. Preece, M. Johnson, G. De Mel, W. Vasconcelos, C. Gibson, A. Bar-Noy, K. Borowiecki, T. La Porta, D. Pizzocaro, H. Rowaihy,
“An ontology-centric approach to sensor-mission assignment,”
In: Knowledge Engineering: Practice and Patterns, LNCS Vol. 5268, pp. 347-363, 2008.