چارچوب هستی شناسی چند زبانه برای سیستم های تحقیق و توسعه مدیریت پروژه

R&D project management systems are developed to facilitate the efficient management of R&D projects in government-funding agencies and universities. They should support R&D information sharing among researchers and research administrators from different nationalities using different languages. This paper presents an ontology-based methodology to the development of R&D project management systems with multilingual supports. In particular, a multilingual ontology is proposed, which consists of domain model, application model, user model and linguistic model. The methodology is used in the development of an R&D project management system for the Innovation and Technology Commission of the Hong Kong SAR Government. The system supports three languages and facilitates R&D information sharing among users with different culture backgrounds and usage preferences.

Research and development (R&D) project management is an important task for government-funding agencies and research institutions. It usually involves several phases, i.e., proposal submission, project selection, approved project administration and project deliverable dissemination. To support the critical decision making tasks in R&D project management, project related information needs to be shared among different parties with different knowledge backgrounds at organizational levels, e.g. project applicants and reviewers with different research interests or speaking different languages. Especially, for some countries/regions that use multiple official languages (such as Hong Kong and Switzerland), project related information and documentation must be stored, processed, and generated in different languages. Thus there is a need to provide R&D project management systems with multilingual supports and organizational knowledge sharing capabilities. Current research on R&D project management shows increasing interests in developing decision models and methods to support components of the project management process (such as project selection) (Klapka and Pinos, 2002, Lee et al., 2009 and Sun and Ma, 2005), or the whole process (Chen et al., 2008, Józefowska et al., 2009 and Valls and Weglarz, 2005). But many methods are not being utilized or have limited impacts on real world R&D project management (Sun et al., 2008 and Tian et al., 2005). To facilitate the use of decision models in real world applications, decision support systems are developed (Klapka and Pinos, 2002, Lin and Hsieh, 2004 and Turban et al., 2007). However, these systems can hardly support multilingual information processing and knowledge sharing among parties with different backgrounds at the organizational level. Information systems with multilingual support have been studied in several application domains, such as in medicine (Boyer et al., 2001, Dejean et al., 2005 and Lu et al., in press) and in astronautics (Bensaid and Astor, 2002 and Walker et al., 2002). From these studies, it can be seen that a domain-specific multilingual terminology (terminology is regarded as a collection of terms in a specific domain) is very important in supporting the multilingual information processing and sharing. However, no multilingual terminology is currently available for R&D project management. Furthermore, “terminology” is only a “vocabulary of a domain/field/discipline/specialized subject” (Temmerman & Knops, 2004), with more emphasis on language-specific “terms” instead of the “concepts” behind it. With the current evolution in the artificial intelligence research, ontology engineering is becoming a popular technology to enhance terminology. An ontology is similar to a dictionary or glossary, but with a greater detail and structure that enables computers to process the ontology’s content ( Fensel, 2004 and Plisson et al., 2007). It consists of a set of concepts, axioms, and relationships that describe a domain of interests, and represents an agreed-upon conceptualization of the domain’s “real world” setting. Thus, ontology can facilitate knowledge sharing within a specific domain (such as R&D project management) at the organizational level. In this paper, a multilingual ontology framework is proposed for the development of R&D project management systems, where domain model, application model, user model and linguistic model are designed to handle related knowledge in R&D project management. The framework and corresponding methodology is applied in the development of a R&D project management system in the Innovation and Technology Commission (ITC) of the Hong Kong SAR Government. The system supports three languages (i.e. English, Traditional Chinese and Simplified Chinese as they are the official languages in Hong Kong), as well as the information sharing among different user groups (e.g. project applicants, project reviewers and administrators) from all over the world. Section 2 of the paper presents the multilingual ontology framework and corresponding methodology. The application to ITC is reported in Section 3, including a project management workflow and the system architecture. Finally, Section 4 summarizes and discusses the results of our work.

This paper presents an ontology-based methodology for R&D project management with multilingual support, which conceptualizes related knowledge into domain model, application model, user model and linguistic model. It forms the necessary basis for multilingual information processing and sharing. The method has been implemented and tested in the ITCFAS, which is an R&D project management system built for ITC in Hong Kong. The method shares the R&D project information and relevant domain knowledge among users with different knowledge backgrounds and different language preferences (i.e. English, Simplified Chinese and Traditional Chinese). It also helps to generate a tailored-made user interface with multilingual supports. The method can be generalized to domains other than R&D project management, since the multilingual ontology and the system architecture are not limited to project management domain. It is also easy to extend it because of the modularity of the ontology structure. Future research could extend the linguistic model to contain context-sensitive information and extend the user model to observe and analyze users’ behavior.