Aligning Knowledge Development between Innovation-Driven Context and Knowledge Organization Systems

1. Aligning Knowledge Development between Innovation-Driven Context and Knowledge Organization Systems Tobias Ley JörgenJaanus Tallinn University i-KNOW 2013, the International Conference on Knowledge Management and Knowledge Technologies; September 6th 2013

2. Starting Point Underlying Theories Concept Compounding Sample Issues Knowledge Alignment Model Conclusions

3. Starting Point New concepts emerging through knowledge maturing process having direct impact on enterprise Knowledge Organization Systems. The shift of focus from creation to addition of new ideas and concepts. Longitudinal approach was selected as a case study that follows development of new concepts over time. Since innovation is a result of the combination of existing and new knowledge, the seamless integration of knowledge creation and usage is essential for the continuous innovation. (Xu J.; Houssin R.; Caillaud E.; Gardoniacro M)

4. Underlying Theories Knowledge Maturing as goal-oriented learning at collective level. Knowledge maturing process consists of five consecutive stages: expressing ideas, distributing in communities, formalizing, ad-hoc learning and standardization. (Maier, R.; Schmidt, A.) An ontological change as any change in the definition of the items of the ontology's vocabulary such as the concepts, their properties and the relations between them. (Hamdani) Knowledge Organization Systems as expression of semantic meaning through classification logic. There are four KOS that can be used to model and organize concepts and to describe terms semantically: controlled vocabularies, taxonomies, thesaurus, ontologies.

5. Concept Compounding Due to the ubiquitous Knowledge Organization Systems integrated to a vast number of applications integration of the new concept to the data structure becomes crucial proof of the new concept. Establishing a new concept is a learning process where the term has to be negotiated. Defining the terms in data management is primarily about setting the relations to other entities and attributes [Chisholm]. It is not possible to provide an adequate definition unless there is an in-depth understanding of the classification logic within the project team or within the broader community.

6. Business Projects • Adding customer service functionality over internet; • Implementing compliance requirement for handling segregation of duties; • Additional data has made it possible to implement more precise costing calculation; • Due to the high turnover of maintenance staff the service is outsourced; • Masterdata structures are changed and made more complex for party data; • Personal appraisal is standardized across the organization

7. Sample Issues Sub-type addition. The new and compounding concept leads to the ontological change. Establishing subtype hierarchy is a tool for managing special properties of certain group of instances. k. Business rules. Compounded concepts are functioning in methodic role. We determined the business rules which have to be followed in task profile according to the task model and link those business rules to types and subtypes on which they are operating on. Consistency considerations. For more business critical domains where the ontology is predominantly data driven the specific concepts have been more consistently used and defined across structured but as well as unstructured information after compounding.

8. Knowledge Alignment Model

9. Conclusions Compounding services lead to retrieval and presentation of associative concepts. It works by relating the emerging concept to the existing concepts by setting criteria on common attributes, class relations and associative relations. Process modeling services include developing knowledge intensive processes by enriching them with business rules. It enables collective information mapping, retrieval and annotation based on the underlying KOS model. Association services enable to annotate unstructured information by tag recommendation system which is based on real time data driven ontology.

10. Conclusions Compounding services lead to retrieval and presentation of associative concepts. It works by relating the emerging concept to the existing concepts by setting criteria on common attributes, class relations and associative relations. Process modeling services include developing knowledge intensive processes by enriching them with business rules. It enables collective information mapping, retrieval and annotation based on the underlying KOS model. Association services enable to annotate unstructured information by tag recommendation system which is based on real time data driven ontology. Ontology development has to comply with the regularity of knowledge creation and has to focus on cyclical development of knowledge.

11. Ontology development has to comply with the regularity of knowledge creation and has to focus on cyclical development of knowledge. Thank you!