Development of an Ontology-based Expert Recommendation System for Product Empirical Knowledge Consultation

Abstract

Product lifecycle, a knowledge-intensive process, consists mainly of market analysis, product design and process development, product manufacturing, product distribution, product in use, post-sale service, and product recycling. Performing and achieving each activity or its supply chain activities require product empirical knowledge at different levels to resolve product-related problems or make certain decisions. However, effective consultation and sharing of tacit product empirical knowledge would greatly enhance product market competitiveness. Therefore, effectively and correctly sharing product empirical knowledge from domain experts in different organizations require developing an expert recommendation system for product empirical knowledge consultation, which is priority concern for product knowledge management. This study develops an expert recommendation system for product empirical knowledge consultation by using ontology to matchmaking of the required consultative experts quickly and correctly for the product empirical knowledge requester, thus sharing required product empirical knowledge by interpersonal communication to resolve product-related problems efficiently. The tasks involved in this study include: (i) designing an expert recommendation process for product empirical knowledge consultation, (ii) developing an expert recommendation method for product empirical knowledge consultation, and (iii) implementing an expert recommendation mechanism for product empirical knowledge consultation.

A novel two-stage method for acquiring engineering-oriented empirical tacit knowledge

“…Knowledge acquisition is the most time-consuming process and the bottleneck in constructing a knowledge-based system (Chen and Rao 2008;Chen, Chen, and Wu 2010). ETK, for its close relationship with the expert's commitment, action and specific context, is regarded as a challenging task to be described, obtained and disseminated (Nonaka, Toyama, and Konno 2000;Kothari et al 2012).…”

“…Ontology (Cochrane et al 2009;Chen 2010Chen , 2011Chen, Chen, and Wu 2010), cognitive map (CM) (Noh et al 2000), case-based reasoning (CBR) (Wang and Hsu 2004;Dias 2007) and social network analysis (SNA) (Kim, Suh, and Jun 2011) are the main methods for tacit knowledge acquisition. Automatic or semi-automatic acquisition systems, such as TTKM (Team Tacit Knowledge Measure for software developer) (Ryan and O'Connor 2009), TKAI (Tacit Knowledge Acquisition Info-Structure for physician) (Abidi, Cheah, and Curran 2005), MediKES (Medical Knowledge Elicitation System for physician) (Ting et al 2011) and KOATING (Knowledge discovery and data mining integrated framework for project team members) (Choudhary et al 2011) have been developed and validated.…”

“…Engineering-based techniques and methods, especially artificial intelligence (AI) approaches, such as Ontology (Cochrane et al 2009;Chen 2010Chen , 2011Chen and Wu 2010), case-based reasoning (CBR) (Wang and Hsu 2004;Dias 2007) and cognitive map (CM) (Noh et al 2000), are widely used to model and acquire tacit knowledge. The application fields of these knowledge acquisition approaches include manufacturing (Zhou 2004), project management (Koskinen, Pihlanto, and Vanharanta 2003), health care (Abidi, Cheah, and Curran 2005;Ting et al 2011) and decision-making (Noh et al 2000).…”

Taxonomy of Gas Turbine Blade Defects

“…The pressing need is for inclusion of an expert system. Several researchers have explored the feasibility of an ontology-based expert system for 'pest and disease management' [48,49], 'analysis of coffee beans' [50], 'suspicious transactions detection' [51], 'process planning' [52], 'product consultation' [53], 'financial rating' [54] and 'medical diagnosis' [55][56][57]. For the development of an expert system, we recommend to first determine the data properties and restrictions and create rules using Semantic Web Rule Language (SWRL).…”

Intelligent knowledge recommending approach for new product development based on workflow context matching

“…To help in understanding knowledge in the collaborative environment, Ahn et al (2005) present a knowledge context model, named “KC-V,” which facilitates the use of contextual information in virtual collaborative work; Ammar-Khodja et al (2008) present a knowledge support approach in the design phase, and they use a knowledge engineering process to structure the information and its use and deploy a knowledge capitalization process based on the enrichment of methodology and tools oriented to knowledge-based engineering application methodology to support the integration of process planning knowledge in a CAD system; the most recent research with respect to process-centric knowledge management falls into information collaborative filtering (Zhen et al, 2009a), recommender system in workflow space (Zhen et al, 2009b), document recommendation based on the knowledge flow technology (Liu et al, 2012a), and so on; Yuh-Jen Chen et al (2010) proposed an expert recommendation system for the product lifecycle, and the work uses the ontology to find required consultative experts quickly and correctly for the product empirical knowledge requester.…”