Knowledge is a critical part of any pro-active and creative business strategy but product support has not been viewed as a knowledge intensive process
Aim: Model product support based on knowledge engineering methodology
Background and related work
Product support is "everything necessary to allow the continued use of a product" (Pham et al. 1999)
Interactive Electronic Technical Manuals (IETMs)
Intelligent Product Manuals (IPMs)
Electronic Performance Support Systems (EPSSs)
Integration of knowledge engineering practices in product support systems
Reasoning mechanisms
Knowledge base
Semantic data modelling
Previous work does not follow a uniform approach towards the development of knowledge bases →Ontological principles are utilised in product support knowledge modelling
Knowledge modelling approach
Product support knowledge sets
Product knowledge
Task knowledge
User knowledge
Data to Knowledge
Raw data is gathered
Common relations among data items are identified →Organised information
Developed structures are further analysed to make them available for productive use
Knowledge modelling approach-continued
Conceptual modelling → Formalise domain knowledge in a high-level, yet authoritative way
First phase: Objective is to organise data in a way that ensures homogeneity and validity of the resulting information
Use of ontological principles and KE notions: Concept, Instance, Relation
Creation of new structures: Domain, connector
The architectural model is developed at the end of the first phase
Second phase: Objective is to put the structured information into productive use, within the context of product support
Architectural model is enriched with two notions: Knowledge-specifier and arc
Connectors are characterised as active or inactive: Connector-dependence measure
The functional model is developed at the end of the second phase
Implementation
The knowledge base is developed using the knowledge model described above, populated with instances and their associations
The Protege ontology editor has been used
Currently, the knowledge base contains 278 frames, including 75 concepts, 80 slots, 22 facets, and 101 instances and part of it is depicted in the next slide
Implementation-continued
Conclusions and future work
The approach formalises the knowledge contained in a product support system with the use of ontological principles
Product support responsiveness is improved by prescribing the knowledge sets required and how these should be represented
Interoperability is advanced by creating an ontology (the knowledge model) that can be shared and reused by other systems
This work is a step towards formalising product support as a knowledge intensive process
Futire work includes the development of a knowledge engineering framework where the ontology will be one of the major components
