The document discusses the Shark Framework, which implements a model for knowledge exchange in a peer-to-peer network. The framework allows peers to share knowledge based on their interests and contexts. It establishes connections between peers, negotiates knowledge to share, and facilitates the extraction and assimilation of knowledge particles. The Shark Framework is being developed to enable applications like collaboration platforms, mobile communities, and distributed, evolutionary ontologies.
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Building Context Aware P2P Systems with the Shark Framework
1. Prof. Dr. Thomas Schwotzer
Computer Science / Mobile Applications
thomas.schwotzer@fhtw-berlin.de
Shark Framework
(Building Context Aware P2P
Applications)
work in progress
2. TOC
• Knowledge
• Knowledge Exchange
• A model of Knowledge Exchange Process
(Shark)
• A knowledge exchanging software engine
(Shark Engine / Shark Framework)
• Examples
• Status
• Summary
3. Boring....
• Some of your might know Shark
– 2001 – 2006 TU Berlin:
• How to apply Semantics to Mobile World
• Mobile Shared Knowledge
• 1st paper 2002, several technology studies, some
open source projects started
• 2006 PhD
• work stopped
• Other scientists know such situations :-(
• Since April 2008 relaunch
– still an issue
– ocean of time, enthusiastic people/students
4. Message
• Shark Framework will be finished
• Will be maintained
– at least in the next 28 years
• This isn't and won't be my project
• Shark stands for Shared Knowledge
• Let's share it
• Open Source with LGPL (sourceforge)
• www.sharksystem.net
5. Knowledge
• AI / Knowledge Representation:
– An ontology is / contains / comprises
knowledge
– A Topic Map is knowledge
– Knowledge can be stored in a Topic Map
– set of facts (e.g. represented by PROLOG)
• Definition by structure
6. Knowledge (2)
• Knowledge Management
– Knowledge is something that helps people to
perform a task / to solve a problem
– Process oriented view on knowledge
– BTW: subject isn't anything!
• Somebody must be interested in it!
No intelligent life -no subjects.
• Implications:
– A document can be knowledge for person A
but just (electronic) paper for B , e.g.
• due to lack of background knowledge
• can't read the format no PDF reader available
7. Knowledge (3)
• Implications:
– A document can be knowledge for person A
but just (electronic) paper for B , e.g.
• due to lack of background knowledge
• can't read the format no PDF reader available
• can't understand the spoken, programming,
description or whatever language
8. Knowledge (4)
• Is a document D knowledge?
– If it helps a person A in a given situation – yes
D is knowledge for A in this situation
– If not: D is no knowledge
• It depends on the context
– issuer, receiver, current situation (in its broadest
possible sense)
9. Knowledge in Topic Maps
• Information resources can be knowledge
– Can contain descriptions that help
• An association of Topics can be knowledge
– Can help to find relations or IR
• Topics
– Can be knowledge
if representing subjects that help
– Can be context
and help to find knowledge
10. Is knowledge true?
• With given definition – it's impossible to
decide
• No objective independent instance which
could decide
• Semantic networks (e.g. Topic Maps)
represent meanings / statements of the
authors
• Known concept: Reification
11. Knowledge – a picture
Context
Person
Information
Statement
Knowledge Particle
= Statement + Issuer
12. Knowledge Exchange - Example
2 „That's what I mean“ „1… that's interesting“
3 „I have some documents about it. W ant to have look?“
4 „Please.“
Mobile Person Mobile Person
5 „Sounds good. Thanks!
“
13. Steps
• Negotiation
– Who has information about what topics
– Who is interested and allowed to send/receive
information
– Implicitly: take context into account
– Leads to an exchange context
• Knowledge Exchange
14. Different to Knowledge Retrieval
• Simple query doesn't produce knowledge
• Full text search on e.g. “music”
• semantic search (e.g. by TMQL) not
fundamentally better
• Context is not described explicitly
– Background knowledge
– Situation
– ...
15. Knowledge Exchange Process
potential sender potential receiver
KB KB
remote identity remote identity
+ +
remote interests Assimilation remote interests
+ Extraction +
sending interests receiving interests
+ +
Knowledge
environment = environment
Particle
(eavesdropping, ..) (eavesdropping, ..)
* I confess: The term assimilation is stolen from the Borg in Star Trek. Hope they'll never find out.
16. Extraction / Assimilation
• Extraction
– Process creates a knowledge
– wants receiver to integrate this knowledge
– A sender can
• lie
• isn't an expert
• Assimilation
– Process that integrates (parts) of received
knowledge
17. Knowledge Exchange Protocol (KEP)
• Interest
– exposes topics of which knowledge is welcome
• Offer
– exposes topics of which knowledge can be sent
• Accept
– sent from a receiver to a dedicated sender
– sents a number of topics
• Insert
– sent from a sender to a dedicated receiver
– Knowledge particle
18. KEP Example 1
Peer Musik / * Musik / * Peer
S R
Establish connection / Identifying
interest(musik)
offer(musik)
accept(musik)
extract(R, Musik);
insert(KnowledgeParticle kp)
assimilate(S, kp);
19. KEP Example 2 (mobile leaflet)
Peer Musik / * Musik / * Peer
S R
Establish connection / Identifying
interest(musik)
extract(R, Musik);
insert(KnowledgeParticle kp)
assimilate(S, kp);
20. KEP Example 3 (hide interests)
Peer Musik / * Musik / * Peer
S R
Establish connection / Identifying
accept(*)
extract(R, Musik);
insert(KnowledgeParticle kp)
assimilate(S, kp);
21. Shark Data Model (in UML, sketch)
Topic 1..* Peer
1
1..*
*
Information Interest
22. Shark Data Model (as TM)
Type Peer Peer
Topic1 A B
Type
Remote Peer
Topic2 Peer
Topic Anonymous
Sending
Receiving
Interest
T represents
a special interest
23. Shark Peer
• Software
• Implements extraction and assimilation
• Implements KEP
• Manages Knowledge Ports which store
interests
• Process
– Observes environment
– If remote peer is detected:
– run KEP (in defined flavour)
24. Autonomy
• Exchanges knowledge only based on rules
described in KPs
• Rules can be changed locally – no
interaction with any server required
25. Flow of knowledge
Alice Bruce
I agree
new idea
I think
Alice I think
Bruce
author author
Alice
Externalization
26. Collaboration
M-TM-P
M-TM-P M-TM-P M-TM-P
M-TM-P
company /
institute working
(trusts its TM
experts)
Topic Maps
expert
member /
employees
27. Knowledge Flow Management
M-TM-P
TM
M-TM-P M-TM-P M-TM-P
TM TM
M-TM-P
company /
institute working TM
(trusts its TM
experts)
Topic Maps
expert
member /
employees
37. Work in progress
• Implementation started April, 2008
• Shark-FW-Core exists
• KEP exists, used exchange format
– compressed proprietary format
– Topic Maps
• Protocols
– TCP, UDP work
– BT Prototyp
• Knowledge Bases
– Filesystem – Prototyp
– tinyTIM – implementation has begun
38. Next steps / priority list
• Applications
– Collaboration platform
– Mobile Community Application
• Knowledge Base
– J2ME
(revive the TM4J2ME project (sourceforge)
– Jena-FW (RDF) (I'll be a traitor, sorry!!)
• Protocols
– Stable Bluetooth implementation
– HTTP
39. Distributed evolutionary Ontologies
• Knowledge can be
– Information resources
– Topics and Associations
• A P2P Knowledge Exchange can lead to
changes in Topic Maps
• Kind of evolutionary process
– Any receiver can accept or drop changes
– “survival of the fittest concepts”
– Might lead to a drift and groups of peers
sharing same / similar ontologies
40. Summary
• Shark model describes the process of
knowledge exchange
• Shark Framework implements this model
• basis for number of applications
• Buzzwords for Shark Applications
– Semantic Grid Applications
more specific mobile Topic Grid Apps
– context aware P2P Apps
