Discovering market trends in the biotechnology industry

Authors:
Haralampos Karanikas;George Koundourakis;Ioannis Kopanakis;Thomas Mavroudakis;Nikos Pelekis
Affiliations:
Knowledge Management Lab., National & Kapodistrian University of Athens, Ano Ilisia 15771, Athens, Greece.;General Secretariat for Information Systems, Ministry of Economy and Finance, Nikis 5-7, 10180, Athens, Greece.;Technological Educational Institute of Crete, Psillinaki 42, Ierapetra, GR-72200, Crete, Greece.;Knowledge Management Lab., National & Kapodistrian University of Athens, Ano Ilisia 15771, Athens, Greece.;Department of Informatics, University of Piraeus, Piraeus, 80 Karaoli-Dimitriou St., GR-18534 Piraeus, Greece
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2011

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Abstract

In this paper we describe our approach to discover trends for the biotechnology and pharmaceutical industry based on temporal text mining. Temporal text mining combines information extraction and data mining techniques upon textual repositories and our main objective is to identify changes of associations among entities of interest over time. It consists of three main phases; the Information Extraction, the ontology driven generalisation of templates and the discovery of associations over time. Treatment of the temporal dimension is essential to our approach since it influences both the annotation part (IE) of the system as well as the mining part.