Abstract
Ontologies provide the means for supporting business intelligence (BI) and information management through the interpretation of unstructured content. On the basis of the semantics of ontologies, information can be extracted from natural language texts, and on a further level of processing knowledge that facilitates BI can be discovered. However, in order to act this way, ontologies need to be properly modelled and evolved so that they are constantly aligned with changes that occur in the real world. This paper presents a framework for modelling the temporal aspects of a semantic knowledge base with direct impact on the BI process.
Acknowledgements
The authors would like to thank the CEO of Biovista, Dr. Andreas Persidis, for providing the case study and its supporting material, including requirements, data sets, and helpful feedback on the paper.
Notes
4 Note that ‘[‘ indicates a closed space, while ‘)’ indicates an open space.
Additional information
Notes on contributors
Alexander Mikroyannidis
Alexander Mikroyannidis is a postdoctoral research fellow in the Knowledge Media Institute of the Open University. His interests are primarily related to knowledge management and applications of Semantic and Social Web technologies. He has been highly active in the FP5, FP6, and FP7 European funding schemes, through the projects ROLE (ICT-2009-231396), DEMO-net (ICT-2006-027219), CASPAR (ICT-2005-033572), and PARMENIDES (IST-2001-39023).
Babis Theodoulidis is a senior lecturer at Manchester Business School. He investigates the modelling, analysis, and management of information within the context of business information systems and is more recently focusing on service industries. His research work has been supported by grants from the U.K. and European funding bodies and also directly by the industry. He has been involved extensively with European projects since the early days of ESPRIT in 1990 and he has been the project manager for the IST project PARMENIDES (IST-2001-39023).