Development of a method for ontology-based empirical knowledge representation and reasoning
Introduction
The global economy has shifted recently from a manufacturing-based value system to a knowledge-based one. Knowledge is essential for entrepreneurial success in the knowledge economy era. Capable of deciding the life or death of an enterprise, knowledge application has become pivotal for enhancing entrepreneurial competitiveness. Consequently, an effective means of improving the competitive advantage of an enterprise is to promote its organizational knowledge value through knowledge accumulation and sharing, as well as effective management of the accumulated empirical knowledge from employees in an enterprise. The increasing importance of an enterprise to implement knowledge management [4], [9], [13] in the knowledge economy era is thus apparent.
Enterprise knowledge management can be implemented as either a systematization strategy or a personalization strategy [13], [26]. Systematization strategy largely focuses on managing explicit knowledge and increasing the dispersion and distribution of explicit knowledge through information systems. Meanwhile, personalization strategy allows experts to own tacit knowledge by cooperating and communicating with a certain expert. Additionally, tacit knowledge [7], [12] generally symbolizes an enterprise value and is generally hidden inside of personal mental models. The inability to transfer tacit knowledge to organizational knowledge (i.e., explicit knowledge) would cause it to disappear after knowledge workers leave their post, ultimately losing important intellectual assets for enterprises. Therefore, enterprise attempting to create higher knowledge value are highly concerned with how to transfer personal empirical knowledge inside of an enterprise into organizational explicit knowledge by using a knowledge-based system to manage and share such valuable empirical knowledge effectively.
Recent studies [2], [17], [30], [32], [34], [36], [37] indicated that most knowledge-based systems cannot effectively address the representation, storage and reasoning for empirical knowledge to offer accurate and comprehensive empirical knowledge for knowledge requesters. This circumstance incurs a bottleneck when sharing personal empirical knowledge in an enterprise.
Ontology [5], [8], [10], [11], [19], [24] refers to a consensus that defines an entity, attribute and relationship among knowledge concepts within a specific domain using explicit descriptions and specifications that present an interoperable format understandable by both humans and machines, thereby it can be used for building knowledge-based systems.
This study develops a method of ontology-based empirical knowledge representation and reasoning, which mainly uses OWL (Web Ontology Language) to represent empirical knowledge in a structural manner to help knowledge requesters clearly understand the empirical knowledge. An ontology reasoning method is subsequently adopted to deduce empirical knowledge in order to share and reuse relevant empirical knowledge effectively. Specifically, this study involves the following tasks: (i) analyze characteristics for empirical knowledge, (ii) design an ontology-based multi-layer empirical knowledge representation model, (iii) design an ontology-based empirical knowledge concept schema, (iv) establish an OWL-based empirical knowledge ontology, (v) design reasoning rules for ontology-based empirical knowledge, (vi) develop a reasoning algorithm for ontology-based empirical knowledge, and (vii) implement an ontology-based empirical knowledge reasoning mechanism.
The rest of this paper is organized as follows. Section 2 defines the ontology-based representation model for empirical knowledge based on the identified characteristics of empirical knowledge. Section 3 then describes the ontology-based reasoning method for empirical knowledge. Next, Section 4 presents an example of financial diagnosis of an enterprise to illustrate how to implement the proposed method of ontology-based empirical knowledge representation and reasoning. Section 5 summarizes the results of implementing a prototype ontology-based empirical knowledge reasoning mechanism. Conclusions are finally drawn in Section 6, along with recommendations for future research.
Section snippets
Design of ontology-based empirical knowledge representation model
This section analyzes the characteristics of empirical knowledge and then describes the design of an empirical knowledge representation model as well as empirical knowledge concept schema by using ontology techniques.
Development of ontology-based empirical knowledge reasoning method
Based on the designed ontology-based empirical knowledge representation model in Section 2, this section develops related techniques to empirical knowledge reasoning, which involves “OWL-based empirical knowledge ontology establishment”, “ontology-based reasoning rules design for single-layer empirical knowledge”, “ontology-based reasoning rules design for cross-layer empirical knowledge”, and “reasoning algorithm design for ontology-based empirical knowledge” as well.
Illustrative example of an enterprise's financial diagnosis
Based on the proposed method of ontology-based empirical knowledge representation and reasoning, the applicability and feasibility of the proposed method are demonstrated using an illustrative example of enterprise's financial diagnosis.
Ontology-based empirical knowledge reasoning mechanism implementation
Based on the above developed method of ontology-based empirical knowledge representation and reasoning, this section uses the software Protégé 3.4 Beta to construct an ontology-based empirical knowledge reasoning mechanism. The implementation environment and results with a financial diagnosis case are described in the following subsections.
Conclusions and further research
This study developed a method for ontology-based empirical knowledge representation and reasoning using ontology techniques to help knowledge requesters to retrieve empirical knowledge for their problem-solving and decision support. Therefore, the tasks involved in the development include: (i) analyzing characteristics for empirical knowledge, (ii) designing an ontology-based multi-layer empirical knowledge representation model, (iii) designing an ontology-based empirical knowledge concept
Acknowledgements
The author would like to thank the National Science Council of the Republic of China, Taiwan, for partially supporting this research under Contract No. NSC98-2221-E-327-039.
Dr. Yuh-Jen Chen is currently an Assistant Professor of Department of Accounting and Information Systems, National Kaohsiung First University of Science and Technology, Taiwan, ROC. He received his Ph.D. and MS degrees in Institute of Manufacturing Information and Systems of National Cheng Kung University in 2005 and 2001 respectively, and gained his BS degree from the Department of Applied Mathematics of Chung Yuan Christian University, Taiwan, ROC, in 1999. His current research interests
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Dr. Yuh-Jen Chen is currently an Assistant Professor of Department of Accounting and Information Systems, National Kaohsiung First University of Science and Technology, Taiwan, ROC. He received his Ph.D. and MS degrees in Institute of Manufacturing Information and Systems of National Cheng Kung University in 2005 and 2001 respectively, and gained his BS degree from the Department of Applied Mathematics of Chung Yuan Christian University, Taiwan, ROC, in 1999. His current research interests include Enterprise Information Systems, Decision Support Systems, Knowledge Engineering and Management, and Service Science.