Management of Distributed Knowledge
Sources for Complex Application Domains
Meike Reichle, Kerstin Bach, Alexander Reichle-Schmehl
and Klaus-Dieter Althoff
University of Hildesheim
{lastname}@iis.uni-hildesheim.de

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Outline
• Motivation
• Knowledge Modularization
• Knowledge Map
• Classification of Knowledge Sources
– Knowledge Source Properties
• Conclusion and Outlook

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Motivation
• Knowledge-based systems deal with
increasingly complex application domains
• Distributed, knowledge-based systems
– Distributed knowledge processing
– Distributed knowledge acquisition
• Realisation of distributed, knowledge-based
systems using well-known AI techniques

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The docQuery Project
• Travel Medicine
– Prevention, management and research of travel
related medical aspects
– Interdisciplinary: Requires expertise in other areas like
geography, activities, etc.
• Our main goal within the docQuery project
– Provision of individualized and reliable information
– On-demand query processing
– Up-to-date information

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SEASALT
• Sharing Experiences using an Agent-based System
Architecture LayouT
• Instantiation of the CoMES (Collaborative Multi-Expert-
Systems) approach
• Features
– Application-independent architecture
– Knowledge acquisition from a web-community
– Knowledge modularisation,
– Agent-based knowledge maintenance

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SEASALT

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SEASALT

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SEASALT

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SEASALT

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SEASALT

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SEASALT

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Knowledge Modularisation
• Knowledge Line (KL) within SEASALT
– KL consists of complex knowledge in smaller,
reusable units (knowledge sources)
• Distribution of knowledge
– Reflects structure of complex (interdisciplinary)
domains
– Facilitates knowledge acquisition
– Facilitates knowledge maintenance

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Knowledge Line

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Knowledge Sources
• Topic Agents + external sources
• Contain different kinds of information
– Multiple knowledge sources for the same purpose
• Knowledge sources are accessed dynamically
– according to their properties
• Retrieval results (can) serve as input for a
subsequent query

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Knowledge Map: Motivation
• Term originates in Davenport’s and Prusak’s
work on Working Knowledge1
• Organises all available knowledge sources
– Who is the expert on a certain topic?
• Coordination Agent (Broker, Mediator)
– Access to knowledge sources
– Combines retrieved information
– Uses Knowledge Map Thomas H. Davenport and Laurence Prusak.
1
Working Knowledge: How Organizations
Manage What they Know. Harvard
Business School Press, May 2000.

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Knowledge Map: Definition I
• Knowledge Map KM consists of a number of Knowledge
Sources KS:
KM= { KS 1 , KS 2 , KS 3 , .. . KS n }
• A Knowledge Source KS consists of a knowledge base
KB and an interface I:
KS= { KB, I }

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Knowledge Map: Definition II
• Dependencies between the Knowledge Sources
– Input/Output dependencies enabling a subsequent retrieval
• Constraints on the Retrieval
– Constraints over all Knowledge Sources
→ Availability, Costs, etc.
• Individual Retrieval Graph
– Representing requested knowledge sources for an individual
query

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Knowledge Map: Example

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Computing Retrieval Graphs
• Computed based on
– The information a user gives in an individual query
– Pre-defined constraints
– Knowledge Source dependencies
• A-priori computation of the retrieval path
• Modified Dijkstra2 algorithm to determine an
optimal route over the graph
2
Edsger W. Dijkstra. A note on two problems
in connexion with graphs. Numerische
Mathematik, 1:269–271, 1959.

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Classification of Knowledge Sources
• Different properties referring to
– Meta-information
– Content
• Complex Knowledge Source properties
– Compound properties

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Meta-Properties
• Access Limits:
– Number of requests per time unit, e.g. Projekt
Deutscher Wortschatz3
• Format:
– XML, HTML, data base tables, pure text, ...
• Syntax:
– HTTP, SQL, agent, web service, ...
• Trust / Provenance:
– Trustworthiness and reliability knowledge sources
3
http://wortschatz.uni-leipzig.de/

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Content Properties
• Content:
– Semantic description: What knowledge is provided?
• Coverage:
– How good is the knowledge source’s topic covered?
• Completeness:
– How complete is the information offered?
• Up-to-dateness
• Expiry

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Complex Knowledge Source Properties
• Complex properties
– Compound properties as a (weighted) sum of
the presented simple properties
• Example: Quality
– Comprises different aspects
Quality= 2 × Coverage 
2 × Up−to−Dateness 2 × Answer Speed

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Assessment of Knowledge Source
Properties
• Automatically assessable properties
– Speed, language and structure
• Manually maintained properties
– Knowledge engineer assigns property values
• Relations between properties
– Syntax, format, structure and cardinality are partially
related  basic sanity checks of their assigned
values
• Similarity-based reasoning

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Values of Knowledge Source Properties

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Conclusion
• Knowledge modularisation:
 Knowledge Line approach in SEASALT
• Focus on distributed knowledge acquisition
 Dynamic access and assessment of
distributed knowledge sources
• Retrieval over distributed knowledge sources
• Management of distributed knowledge sources

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Outlook
• Retrieval Path computation
– More flexible computation
– Algorithm extension towards a more flexible and
subsequent result dependent routing
– Automated integration of feedback about knowledge
sources
• Application and evaluation in docQuery

Thank you for your attention!
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