knowledge technologies 2001 siemens automation and drive help desk:
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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. - PowerPoint PPT PresentationTRANSCRIPT
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Knowledge Technologies 2001 Siemens Automation and Drive Help Desk:A Knowledge Work-Place with Self-Service
Norman Zimmerempolis NA, Inc.Burlington, MA
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Expert
Call-Center
Customers
Same sort of ProblemsSame expert
Same sort of Problemsmany different experts andagents
Lots of differentproblems and customers
1st -Level
2nd -Level
3rd -Level
Call-Center Pyramid
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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.
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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.
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? !Knowledge-Server Idea
QuestionsAnswers
KnowledgeServer
ContentBase
Knowledge is key to transform Data into InformationKnowledge is key to transform Data into Information
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Motivation
Reuse experience to solve new problemsKnown examples utilize structured data in databasesbut 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?
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In many areas knowledge is stored as weakly structured text:
Frequently Asked QuestionsDocumentationsManualsNotes and CommentsCustomer queriesProposalsand many more ...
Knowledge in Text
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Documents contain a lot corporate knowledgeDocuments have specific characteristics:
restricted topicmostly free textpartly structured (chapters, section, ...)many documents address the same topic
Knowledge in Text
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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
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Example: Query
Q: On my PC the input of a long street name causes a crash. The error message is “Memoryfault”.
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Example: Query
Q: On my PC the input of a long street name causes a crash. The error message is “Memoryfault”.
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Example: Query and Results
Q: On my PC the input of a long street name causes a crash. The error message is “Memoryfault”.
F1: On Windows 3.1 there is not enough memory allocated for the name of the street. This may cause the system to go down.
F2: The PC-Version stores the street name incorrectly.F3: Typing German characters causes a Sun to crash.
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Example: Query and Results
Q: On my PC the input of a long street name causes a crash. The error message is “Memoryfault”.
F1: On Windows 3.1 there is not enough memory allocated for the name of the street. This may cause the system to go down.
F2: The PC-Version stores the street name incorrectly.F3: Typing German characters causes a Sun to crash.
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Analyzing Text
Create a dictionary of relevant termsCreate relations and similaritiesUtilize layers of knowledge:
Keywords: relevant common termsPhrases: application specific termsFeature Values: structured informationThesaurus: relations among keywordsGlossary: relations among phrasesDomain Structure: e.g. products Information Extraction: feature values from text
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Prerequisites
Availability of appropriate documentsthe more the better (initially)extensible
Semi-automatic construction of dictionariesdatabases, other documents
Semi-automatic construction of the knowledge modeldatabases, existing glossaries
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Ideal
Many documents electronically availableHTML, TXT, DOC, PDF, ...
Clearly distinguished topicsspecific application area
Documents correspond to cases1 Case = 1 Document1 Case = 1 Section in a document
Many userscustomers and technicians via WWWin-house teams via Intranet
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Maintain systemand publish
Knowledgeextraction
2nd levelproblem solving
1st levelproblem solving
Customer
Editor
ExpertExpert
Expert
Hotline
Administrator
SKM
solveproblem
problem
request
solveproblem
discuss
discuss
discuss
report report
document
FAQ
maintain
publish
Knowledge Capture Process
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SIMATIC Knowledge manager
www.ad.siemens.de
orenge:Server
Structure
Informa-tion about SIMATICProduct structure
Products
Order no.
Product name
DictionaryInform-ationunits
Simil-arities
Knowledge model
Documents within the customerssupport information
system
SearchResults
Document view
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SIMATIC Knowledge Manager
Search in 20.000 FAQs
CD-Rom & Internetseamless integration
Online since 1998
German & English
FAQ Support
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Call Avoidance = Savings
2.5 Million Dollar Savings in
2.5 Million Dollar Savings in
12 Months
12 MonthsSavin
gs in
Th
ou
san
ds
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Maintain systemand publish
Knowledgeextraction
2nd levelproblem solving
1st levelproblem solving
Customer
Editor
ExpertExpert
Expert
Hotline
Administrator
SKM
solveproblem
problem
request
solveproblem
discuss
discuss
discuss
report report
document
FAQ
maintain
publish
Measurement
• Number of Calls• Time to Solve Problems• Amount of Knowledge• Coverage• User Satisfaction• Cost of Evolution