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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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

1 Thomas H. Davenport and Laurence Prusak. 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:

• A Knowledge Source KS consists of a knowledge base KB and an interface I:

KM= {KS1 , KS2 , KS 3 , .. . KS n }

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 graph2 Edsger W. Dijkstra. A note on two problems in connexion with graphs. NumerischeMathematik, 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 aspectsQuality= 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!

Questions | Suggestions | Comments