Authors:
Jessica Huster
1
and
Andreas Becks
2
Affiliations:
1
Fraunhofer-Institute for Applied Information Technology FIT and RWTH Aachen University, Germany
;
2
RWTH Aachen University, Germany
Keyword(s):
Text mining, Concept-drifts, Ontology evolution.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Strategic Decision Support Systems
;
Web Information Systems and Technologies
Abstract:
Ontologies are used as knowledge bases to exchange, extract and integrate information in information retrieval and search. They provide a shared and common understanding that reaches across people and application systems. In reality, domain specific and technical knowledge evolve over time, and so must ontologies. Creative domains, as for example the home textile industry, are representatives for quickly evolving domains. In this domain it is also important to provide methodologies for the visualisation of knowledge evolution. In this paper we report on our ontology-based trend analysis tool, which supports marketing experts and designers to identify trend drifts, and to compare the analysis results against the ontology. Furthermore means to adapt and evolve the ontology in accordance with the changing domain are provided.