1. Complex Adaptive Knowledge Management System Supervisors: Kurt April (Knowledge Management) Sonia Berman (Databases) Anet Potgieter (Artificial Intelligence)

2. Structure of Talk • Overview of project • Knowledge Engineering • Data Mining • Adaptive Presentation and Visualisation • Questions

3. Project Overview • “Organisations are information rich but knowledge poor” – A Moore • Knowledge Management: • Leveraging Knowledge to create and sustain competitive advantage. • Project Challenge: • Process information to create knowledge that optimizes knowledge transfer to support effective knowledge management. • Strategy: • Use a unique combination of database management, artificial intelligence and visual representation to draw useful concepts from vast quantities of information.

4. Graphical illustration of roles as they fit into the KDD process Presentation of Knowledge to User Domain of Adaptive Presentation & Visualisation Domain of Knowledge Engineering Domain of Data Mining Diagram adapted from The two Crows Corporation: Introduction to Data Mining and Knowledge Discovery. Third Edition

5. Graphical illustration of roles as they fit into the KDD process Presentation of Knowledge to User Domain of Adaptive Presentation & Visualisation Domain of Knowledge Engineering Domain of Data Mining Diagram adapted from The two Crows Corporation: Introduction to Data Mining and Knowledge Discovery. Third Edition

6. Knowledge Engineering • What is knowledge engineering? • What role does it play in the project? • What do I hope to achieve in the project?

7. Knowledge Engineering • Definition • It is the acquisition, validation, representation and explanation of knowledge. • Primary activities • Activities of KE are broad. Only a small subset will be implemented for the project. • Knowledge acquisition • The process of gathering the knowledge to stock the expert system's knowledge base. • Knowledge validation • Objective is to produce knowledge of high integrity • Validation of knowledge to source and expected or known outcomes (close collaboration with data miner)????

8. Knowledge Engineering • Role in project • Information acquisition • Information preparation (support data mining) • Ordered/indexed storage of information and user profile information • Support for data mining • Persistence of data mining deliverables • Ordered/structured information that can be efficiently and easily queried

9. Knowledge Engineering • Questions tackled • Does knowledge engineering effectively support adaptive knowledge management? • Can knowledge engineering further the functional scope of adaptive knowledge management?

10. Knowledge Engineering • Success factors • Responsive support for data mining • Secure, ordered persistence of information, user data and data mining deliverables

11. Graphical illustration of roles as they fit into the KDD process Presentation of Knowledge to User Domain of Adaptive Presentation & Visualisation Domain of Knowledge Engineering Domain of Data Mining Diagram adapted from The two Crows Corporation: Introduction to Data Mining and Knowledge Discovery. Third Edition

12. Data Mining • What is data mining? • Data mining is the step in the knowledge discovery process that consists of applying data analysis and discovery algorithms that, under acceptable computational efficiency limitations, produce a particular enumeration of patterns (or models) over the data. • What is the goal of this component? • Discovery of “Knowledge” • A subset of this task involves trying to establish criteria for evaluating the inherent subjective nature of interestingness in a more objective manner in the given domain of interest. • 3 Main areas that require mining: • Concept Mining of documents • User profiles • System Learning (unlikely to be implemented)

13. Concept Mining • What is Concept Mining? • The concept mining area of the system is responsible for the efficient, timely extraction of knowledge or concepts from the media stored in the system’s databases. • Concepts can be interrelated and are related to user profiles and expected profiles • How will this be implemented? • Mainly mining of concepts from unstructured/text documents • Look at other media including audio, images and video as time permits • Evaluating known techniques to select most appropriate technique • Component based distributed artificial intelligence

14. Profile Mining • Expected Profile vs User Profile • What is the expected profile • The profile that the company expects from a person occupying a specific job i.e., the knowledge required to render a satisfactory (and approaching excellence) job performanance • What is the user profile • The profile that the user actually has dependant on level of education, personal interests and knowledge acquired from company knowledge management system • How will this be implemented? • Expected profile from company job descriptions and users tacit knowledge of job • User profile mined from usage of the system and personnel records

15. Questions tackled • How does adapt to its dynamically changing knowledge in the enterprise • How does it incrementally learn new knowledge • What is the best AI technology to use to learn from and adapt to dynamically changing knowledge

16. Success Factors • The accuracy and depth of concepts mined from the existing resources. • The most important measure of the success will be the feedback provided by the users of the system. • How well can adaptive KM force the 2 profiles to converge for overall sustainability of the company? • How dynamic the learning and adaptive process is?

17. Graphical illustration of roles as they fit into the KDD process Presentation of Knowledge to User Domain of Adaptive Presentation & Visualisation Domain of Knowledge Engineering Domain of Data Mining Diagram adapted from The two Crows Corporation: Introduction to Data Mining and Knowledge Discovery. Third Edition

18. Adaptive presentation and visualization • Adaptive? • Presentation? • Visualization? • Example: • If you are a new user and need to find something amongst hundreds of sources of information you will not want to look at irrelevant documents, being too detailed or too broad • After a while the engine should pick up your patterns and reduce the data retrieved by the data mining engine by tailoring it to user needs and expected profile

19. Why must the interface be adaptive? • Vast amount of data being mined from different sources • This will mean that the detail of the data returned will vary • It may also be “hidden” – not known to anybody as the relationships may never have existed before now • The data may be complex – many sources and duplication leads to data being unusable • Data returned may be unrelated to search

20. What are the problems? • Users have different needs and use tools in different ways • Expected profiles differ from user profiles • Must adapt to these different needs • Use profiles to capture needs • Profiles are then used to display data accordingly

21. Are there any benefits? • Knowledge transfer can be sped up and more effective • Increases the employee’s value and so the company’s value • Less output will be returned • Thus, the system will be faster • Increase attention

22. Related work Agents are used to monitor the user and profile him They then act accordingly and provide the best results Outride now work with Google to implement personalized searching