1. 1 Knowledge Technologies 2001 Siemens Automation and Drive Help Desk: A Knowledge Work-Place with Self-Service Norman Zimmer empolis NA, Inc. Burlington, MA

2. 2 SIEMENS Automation & Drives Process Control Systems Machinery

3. 3 Distributed Organization

4. 4 Expert Call-Center Customers Same sort of Problems Same expert Same sort of Problems many different experts and agents Lots of different problems and customers 1 st -Level 2 nd -Level 3 rd -Level Call-Center Pyramid

5. 5 What is CBR ? Experience is documented as a case. A new problem is solved by adapting the solution of a stored case to the new situation. Case-Based Reasoning (CBR) is a problem solving approach, that applies known solutions of past problems to solve new ones.

6. 6 Examples A doctor remembers past patient records. An advocat argues by precedence. An architect reuses designs of existing buildings. A sales agent explains a new product by referring to satisfied customers. A service technician remembers a similar defect from another machinery.

7. 7 ?! Knowledge-Server Idea Questions Answers Knowledge Server Content Base Knowledge is key to transform Data into Information

8. 8 Motivation  Reuse experience to solve new problems  Known examples utilize structured data in databases  but in most cases there is a lot of existing unstructured information in free text form  Is it possible to apply the CBR paradigm to such text information?

9. 9 In many areas knowledge is stored as weakly structured text:  Frequently Asked Questions  Documentations  Manuals  Notes and Comments  Customer queries  Proposals  and many more... Knowledge in Text

10. 10 Documents contain a lot corporate knowledge Documents have specific characteristics:  restricted topic  mostly free text  partly structured (chapters, section,...)  many documents address the same topic Knowledge in Text

11. 11 Example: FAQ FAQ document Hardware: PC & HP DeskJet 870 Software: Windows 95 Question: My new printer crops graphic print outs. Answer: load and install new printer driver

12. 12 Example: Dictionary ComputerDownPCMachineSunCrashStorageInputWin3.1

13. 13 Example: Ontology ComputerDownPCMachineSunCrashStorageInputWin3.1

14. 14 Example: Synonyms ComputerDownPCMachineSunCrashStorageInputWin3.1

15. 15 Example: Antonyms ComputerDownPCMachineSunCrashStorageInputWin3.1

16. 16 Example: Query  Q:On my PC the input of a long street name causes a crash. The error message is “Memoryfault”.

17. 17 Example: Query  Q: On my PC the input of a long street name causes a crash. The error message is “Memoryfault”.

18. 18 Example: Query and Results  Q: On my PC the input of a long street name causes a crash. The error message is “Memoryfault”.  F 1 :On Windows 3.1 there is not enough memory allocated for the name of the street. This may cause the system to go down.  F 2 :The PC-Version stores the street name incorrectly.  F 3 :Typing German characters causes a Sun to crash.

19. 19 Example: Query and Results  Q: On my PC the input of a long street name causes a crash. The error message is “Memoryfault”.  F 1 :On Windows 3.1 there is not enough memory allocated for the name of the street. This may cause the system to go down.  F 2 :The PC-Version stores the street name incorrectly.  F 3 :Typing German characters causes a Sun to crash.

20. 20 Analyzing Text  Create a dictionary of relevant terms  Create relations and similarities  Utilize layers of knowledge:  Keywords: relevant common terms  Phrases: application specific terms  Feature Values: structured information  Thesaurus: relations among keywords  Glossary: relations among phrases ‘Domain Structure: e.g. products ’Information Extraction: feature values from text

21. 21 Prerequisites  Availability of appropriate documents  the more the better (initially)  extensible  Semi-automatic construction of dictionaries  databases, other documents  Semi-automatic construction of the knowledge model  databases, existing glossaries

22. 22 Ideal  Many documents electronically available  HTML, TXT, DOC, PDF,...  Clearly distinguished topics  specific application area  Documents correspond to cases  1 Case = 1 Document  1 Case = 1 Section in a document  Many users  customers and technicians via WWW  in-house teams via Intranet

23. 23 Example: Document Clear topic sub-structure by products specific vocabulary

24. 24 Knowledge Capture Process

25. 25 Text

26. 26 “Ontology”

27. 27 SIMATIC Knowledge manager www.ad.siemens.de orenge:Server Structure Informa- tion about SIMATIC Product structure Products Order no. Product name Dictionary Inform- ation units Simil- arities Knowledge model Documents within the customers support information system Search Results Document view

28. 28 Analysis of Queries

29. 29 SIMATIC Knowledge Manager Search in 20.000 FAQs CD-Rom & Internet seamless integration Online since 1998 German & English FAQ Support

30. 30 Call Avoidance = Savings 2.5 Million Dollar Savings in 12 Months Savings in Thousands

31. 31 Measurement Number of Calls Time to Solve Problems Amount of Knowledge Coverage User Satisfaction Cost of Evolution

32. 32 empolis BERTELSMANN MOHN MEDIA GROUP Transforming Information into Value