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For a long time, knowledge creation process has been solely associated with human because it is one of the most intellectual processes. However, modern society witnesses the emergence of new sources of knowledge: organizational knowledge and machinery knowledge. Organizational knowledge is created through the interaction among agents in a complex system or organization. Typical research into collaborative knowledge is Nonaka and Takeuchi's (1995) which introduces how knowledge is organizationally created in enterprises and produces successful products and technologies. Machinery knowledge is greatly motivated by two recent factors of information science: deep learning technique and the ‘big data’. Deep learning is machine learning technique that allows computer to learn knowledge from data with very little human assistant. Meanwhile, ‘Big data’ refers to any amount of data that is too large and complex to be processed using on-hand database management tools or traditional data processing applications. With the appearance of these factors, it is expected that machines can generate knowledge beyond human insight.
The emergence of these knowledge sources make knowledge sharing becomes significantly essential. The problem of knowledge sharing was studied by many researchers of knowledge management field that indicated that the organization culture is more important aspect than technical problems (Tang & Zhang, 2008; Widen-Wulff & Ginman, 2004; Widen-Wulff & Suomi, 2003). But the technical problems of knowledge sharing are critical issue, especially the heterogeneity among different sources where one collaborator cannot understand ambiguous knowledge shared by the other.
Ontology and Semantic Web technologies are the answer for heterogeneous problem when sharing and integrating knowledge from different sources. By definition given by Studer et al. (1998), which extends Gruber's definition (Gruber, 1993), ontology is an explicit and formal specification of a conceptualization. Therefore, ontology formally represents knowledge about a world or a domain to allow sharing and incorporating mutual understanding among collaborators in knowledge sharing. They can be used to express knowledge in a computer-interpretable formulation. Specifically, ontologies constructed in a language such as OWL-DL can represent domain knowledge within a description logic (DL) formalism (Smith, Horrocks, Krotzsch, Glimm, & eds, 2012). Then an ontology becomes a logical theory which gives an explicit, partial account of a conceptualization (Guarino, 1998). Then DL-based inference can be used to support knowledge sharing (Guo & Kraines, 2009).
We introduce a comprehensive ontology, named KS Ontology, to represent and manage knowledge about educational program in School of Knowledge Science of JAIST. In the educational program, the requirements for a doctoral student to be qualified are quite complicated and are one of the main concerns of freshmen when entering the program. They often consult their seniors about this problem. However, it also costs much time for them to understand the requirements to prepare and submit the enrollment just after the entrance. This motivates us to construct this ontology to ease the burden of the newcomers. The constructed ontology contains sufficient expressive power to derive and explain implicit knowledge to help students understand the educational program and build their own knowledge about their study plan.
The remaining of this paper is organized as follows. We introduce basic definitions of description logics and their elements. Based on these notations, KS ontology is formalized. We then present implementation and evaluation of this ontology in Protégé ontology development environment. Before concluding this paper, we discuss the related work of applying ontology to education and knowledge sharing in general.