Advanced Knowledge Technologies/Ontologies

Configuration and Mass Customisation

The advent of the mass-customisation (also known as “post-mass production”) paradigm and the increasing and increasingly automated integration of supply chain management tools, especially in B2B e-commerce situations, force the development of high level representation languages to enable effective dissemination and application of product configuration knowledge.

Applications range from intelligent product catalogues that help in selecting individual parts over the fully automatic configuration of large technical systems with thousands of components to current and urgent research areas such as configuration of software or web services. A current topic in view of the near-future scenarios that envision automatic integration of configuration knowledge across supply chains is the development of general configuration ontologies for application integration purposes and the development of ontology languages expressive and yet intuitive enough to provide widespread acceptance among software designers.

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Integrating Private Ontologies

Ontologies provide a mechanism for people to capture the semantic relationships and axioms that define the terminology they use in a machine readable format. This allows for machines to reason and query knowledge to provide context specific results. Ontologies represent an organisations perception of a domains terminology.

As ontologies become more prevalent for information management the need to manage the ontologies increases. Multiple organisations, within a domain, often work together on projects. When separate organisations come together to communicate an alignment of terminology and semantics is required. Ontology creation is often performed independently. This creates problems with alignment and integration, due to the subjective creation process, making it necessary to consider how much each ontology should influence the current decision to be made (i.e. how much two authors perceptions of a domain agree).

To assist with determining influence a trust based approach on authors and their ontologies provides a mechanism for ranking reasoning results. A representation of authors and the individual resources they provide for the merged ontology becomes necessary. The authors can be weighted by trust and trust for the resources they provide the ontology is calculated. This is then used to assist the integration process allowing for an evolutionary trust model to calculate the level of credibility of resources. Once the integration is complete semantic agreement between ontologies allows for the revision of the authors’ trust.

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