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Top1. Introduction
Information systems, especially knowledge management systems and e-learning systems utilize context-sensitive and domain-specific knowledge. In a case of complex nature domains, ontologies are widely used to provide the underlying knowledge structure. We applied this ontology based approach in several research projects in the field of education and e-government such as in Studio system development (Kő, Gábor, Vas, & Szabó, 2008), in SAKE project (Kő, Kovács, & Gábor, 2011) and in Ubipol project (“UbiPOL “, 2009). Studio system(Vas, 2007)is an ontology-based adaptive learning and testing solution, which provides a platform independent learning environment that enables the development of customized qualification programs, based on the individual’s pervious qualifications, completed levels, corporate trainings and practical experiences. SAKE (Semantic-enabled agile knowledge-based e-Government) solution was utilized in several other fields and projects; especially in the investigation of job market needs, educational system supply and managing information overload gained benefit from it (Kő et al., 2011; Matas, 2012; “UbiPOL “, 2009). In SAKE project ontology development was one of the most complex and time-consuming tasks that required professional experience involving a lot of expert discussions and efforts. Ubipol (Ubiquitous Participation Platform for Policy Making) solution is a ubiquitous platform that allows citizens to become involved in policy-making processes (PMPs) regardless of their current location and time. It performs private semantic information retrieval based on an ontology outlined in policies; and ubiquitous data-mining at the device level, along with privacy-preserving data-mining at the server level (Husaini, Ko, Tapucu, & Saygın, 2012; Kő, 2012). In all projects mentioned above, the authors struggled with ontology maintenance and enrichment, because the domain knowledge and the regulatory environment become outdated fast.
Ontologies have been studied for a long time in the fields of semantic technologies, artificial intelligence and knowledge management. Current state-of-the art research in ontologies has focused on the development methods and possible applications of ontologies (Khondoker & Mueller, 2010; López, Pérez, & Amaya, 2000; Pan, Staab, Aßmann, Ebert, & Zhao, 2012). However, there remain many obstacles for the management and enrichment of ontologies (Gasevic, Zouaq, Torniai, Jovanovic, & Hatala, 2011; Miranda, Isaias, & Costa, 2014). Ontology learning, enrichment and maintenance is an ongoing and complex process, with several challenges (Shamsfard & Abdollahzadeh Barforoush, 2003; Wong, Liu, & Bennamoun, 2012; Zouaq, Gasevic, & Hatala, 2011). It has a key role in ontology management; it tackles the issues to turn facts and patterns from the content into shareable high-level constructs or ontologies.
Any ontology update or maintenance can have several consequences. Deleting or adding an ontology object have effect to other objects, it can modify relations, objects and axioms. In a case of the huge number of ontology objects, regular update requires standard process. This paper aims to discuss an ontology enrichment method, based on an innovative text mining solution, namely ProMine. The ontology enrichment process is applied in Studio ontology-based adaptive learning and testing solution in the IT audit domain.