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Knowledge Technologies 2001 Siemens Automation and Drive Help Desk:

Knowledge Technologies 2001 Siemens Automation and Drive Help Desk: A Knowledge Work-Place with Self-Service Norman Zimmer empolis NA, Inc. Burlington, MA. SIEMENS Automation & Drives. Process Control Systems Machinery. Distributed Organization. Expert. 3 rd -Level.

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Knowledge Technologies 2001 Siemens Automation and Drive Help Desk:

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  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. SIEMENS Automation & Drives • Process Control Systems • Machinery

  3. Distributed Organization

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

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

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

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

  10. Knowledge in Text 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

  11. 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 Example: FAQ

  12. Down Crash Computer Machine Sun PC Win3.1 Storage Input Example: Dictionary

  13. Down Crash Computer Machine Sun PC Win3.1 Storage Input Example: Ontology

  14. Down Crash Computer Machine Sun PC Win3.1 Storage Input Example: Synonyms

  15. Down Crash Computer Machine Sun PC Win3.1 Storage Input Example: Antonyms

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

  17. Example: Query • Q: On myPCtheinputof a long street name causes acrash. The error message is “Memoryfault”.

  18. Example: Query and Results • Q: On myPCtheinputof a long street name causes acrash. The error message is “Memoryfault”. • F1: OnWindows 3.1there is not enough memoryallocated for the name of the street. This may cause the system to godown. • F2: ThePC-Version storesthestreet name incorrectly. • F3: TypingGerman characters causes a Sun to crash.

  19. Example: Query and Results • Q: On myPCtheinputof a long street name causes acrash. The error message is “Memoryfault”. • F1: OnWindows 3.1there is not enough memoryallocated for the name of the street. This may cause the system to godown. • F2: ThePC-Version storesthestreet name incorrectly. • F3: TypingGerman characters causes a Sun to crash.

  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. 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 Prerequisites

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

  24. Knowledge Capture Process

  25. Text

  26. “Ontology”

  27. SIMATIC Knowledge manager orenge:Server Knowledge model Search Structure Informa-tion about SIMATIC Product structure Products Order no. Product name Dictionary Inform-ationunits Simil- arities Results www.ad.siemens.de Document view Documents within the customerssupport informationsystem

  28. Analysis of Queries

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

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

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

  32. empolis BERTELSMANN MOHN MEDIA GROUP Transforming Information into Value

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