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MIA An Information System for Mobile Users. Bernd Thomas University Koblenz AI-Research Group. Motivation Architecture & Functionality Agents, Communication & Distribution Information Extraction Using Ontological Knowledge. Outline. The M obile I nformation A gent Project. - PowerPoint PPT Presentation
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MIA: An Information System for Mobile Users
MIAAn Information System for Mobile Users
Bernd ThomasUniversity KoblenzAI-Research Group
3.2.2003 - Agentlink I2A 2
MIA: An Information System for Mobile Users
Bernd Thomas
Outline
Motivation
Architecture & Functionality
Agents, Communication & Distribution
Information Extraction
Using Ontological Knowledge
3.2.2003 - Agentlink I2A 3
MIA: An Information System for Mobile Users
Bernd Thomas
The MobileInformationAgent Project
Clients: WebBrowser, WAP, PDA+GPS
Ambition: Online Web Search and
Information Extraction Location Awareness Anytime Algorithm
Uses Logic (LP): Agents + Ontology
Distributed Multi Agent System
Project Time: 1.1.2000 - 31.10.2002
profile creation
search
constraint search
re-login
start and logout
What is MIA?
A Multi Agent Information System that provides a mobile user with location based informationaccording to his individual interests.
QuickTime™ and a Animation decompressor are needed to see this picture.
3.2.2003 - Agentlink I2A 4
MIA: An Information System for Mobile Users
Bernd Thomas
MIA and its Agents
search
extract
PDA,GPS,Mobile WAP WEB Browser
HTTP
Black-Board
Black-Board
host N
...
GPSDB
GPSDB
User Interests
foreignAgent
queryrequest start
register
start
Paltfom Agent
host n
Paltfom Agent
host 1
classifySpider
AgentBlackboard
Agent
Matchmaker
Server
Agent
KQML
Ontology
Agent
3.2.2003 - Agentlink I2A 5
MIA: An Information System for Mobile Users
Bernd Thomas
Agent Communication
MIA‘s agent system and communication architecture oriented at FIPA
MIA‘s agents use KQML performatives.
Agent platforms: abstraction from machine provide environment for agents
Other Agents can query platform agents for running agents or request starting of agents
MIA‘s agent system and communication architecture oriented at FIPA
MIA‘s agents use KQML performatives.
Agent platforms: abstraction from machine provide environment for agents
Other Agents can query platform agents for running agents or request starting of agents
phase I host A -> platform A : startplatform A -> matchmaker : startplatform A -> blackboard : startplatform A -> server : start
Example Communication Session (System Startup and Search with 3 hosts):
phase II host B -> platform B : startplatform B -> matchmaker : registerhost C -> platform C : startplatform C -> matchmaker : register
phase III server -> matchmaker : ask for blackboardmatchmaker -> server : blackboard addressmatchmaker -> server : recommend blackboardserver -> blackboard : ask for old resultsblackboard -> server : send old resultsblackboard -> matchmaker : subscribe to agent status change
start search server -> matchmaker : ask for spider recommendation
matchmaker -> platform B : create spider agent
spider -> platform B : created
platform B -> matchmaker : send spider address
platform B -> matchmaker : send spider recommendation
server -> spider : start spidering topic/city
server -> spider : send all results for topic/cityspider -> server : starting to searchmatchmaker -> blackboard : there is a new spiderblackboard -> spider : send all results for topic/city
start search server -> matchmaker : ask for spider recommendation
matchmaker -> platform B : create spider agent
spider -> platform B : created
platform B -> matchmaker : send spider address
platform B -> matchmaker : send spider recommendation
server -> spider : start spidering topic/city
server -> spider : send all results for topic/cityspider -> server : starting to searchmatchmaker -> blackboard : there is a new spiderblackboard -> spider : send all results for topic/city
3.2.2003 - Agentlink I2A 6
MIA: An Information System for Mobile Users
Bernd Thomas
Agent Distribution Policy
Distributed MAS has two goals:
1. distribute computation among machines
2. minimize communication between machines
Distributed MAS has two goals:
1. distribute computation among machines
2. minimize communication between machines
MIA uses simple distribution policy:
platform-agent 1: matchmaker, server and blackboard
platform-agent 2-n: ontology-agent and spider-agents are equally distributed
MIA uses simple distribution policy:
platform-agent 1: matchmaker, server and blackboard
platform-agent 2-n: ontology-agent and spider-agents are equally distributed
Load-Balancing:MIA does not use automatic load-balancing,but while the system is online new platform-agents can be added
Load-Balancing:MIA does not use automatic load-balancing,but while the system is online new platform-agents can be added
much communicationless computation
less communicationmuch computation
3.2.2003 - Agentlink I2A 7
MIA: An Information System for Mobile Users
Bernd Thomas
Information Extraction
apply offline learned wrappers (synthesized extraction procedures)
set of predefined pages are examined by offline learned wrappers
online learning of wrappers
for each page found by the spider and positive address containment classification a wrapper is learned.
major problem: absence of examples!
Online and Offline method both learn only from positive examples
Both methods use LGG techniques on feature-terms to learn.
MIA uses two modes to extract information from web pages:
3.2.2003 - Agentlink I2A 8
MIA: An Information System for Mobile Users
Bernd Thomas
IE: Offline Wrapper Learning Wrapper Learning System: for offline learning and integration into the MIA system
Learning Technique:
Document Representation: • logical representation of a DOM-Tree (set of facts)
• each node is represented by a feature term Idea:
• learn relevant features of ancestor and descendant nodes surrounding the relevant nodes for extraction
Method: • learning from positive examples (subtrees) only • LGG on feature terms,• user-based inductive learning
Result: generalized node paths
3.2.2003 - Agentlink I2A 9
MIA: An Information System for Mobile Users
Bernd Thomas
IE: Online Wrapper LearningMajor Problem:
• how to obtain learning examples (example extractions) for unknown pages?
Idea:• use (very strict) address patterns to idenitfy only a few addresses on a page• these few matches serve as learning examples
Document Representation: • list of tokens (feature terms)
Method:• one shot learning (generalize in one step on all examples)• for each page one wrapper is learned
Result: generalized feature-term lists used as left and right delimiters for extraction
3.2.2003 - Agentlink I2A 10
MIA: An Information System for Mobile Users
Bernd Thomas
IE: Extraction Evaluation
Evaluation for online learned wrappers: „self-supervision“: check if extractions match with generalized
patterns derivable from knowledge base semantic cross check: use associated semantic of slots for evaluation
Evaluation for offline learned wrappers: semantic cross check
How does the agent can verify the quality of its extractions?
slot (extracted) check
city match with estimated city name from GPS database or user input
zip code check with zip DB and city slot
search topic and condition
match with similar concept names or instances derivable from ontology
3.2.2003 - Agentlink I2A 11
MIA: An Information System for Mobile Users
Bernd Thomas
MIA‘s OntologyOntological Knowledge useful for:
Web Spidering: keywords from the user profile may not be sufficient
Information Extraction: check correctness of extractions
Ontological Knowledge useful for:
Web Spidering: keywords from the user profile may not be sufficient
Information Extraction: check correctness of extractions
Description Logic used to model ontology for gastronomy & recreation domains
RACER: Renamed ABox and Concept Expression Reasoner (Volker Haarslev, Ralf Möller)
KrHyper (Peter Baumgartner) [WLP2001]: bottom up model generation DL similar language (plus non-monotonic negation, rule based language)
Description Logic used to model ontology for gastronomy & recreation domains
RACER: Renamed ABox and Concept Expression Reasoner (Volker Haarslev, Ralf Möller)
KrHyper (Peter Baumgartner) [WLP2001]: bottom up model generation DL similar language (plus non-monotonic negation, rule based language)
3.2.2003 - Agentlink I2A 12
MIA: An Information System for Mobile Users
Bernd Thomas
Ontology
partial TBOX of MIA‘s gastronomy ontologypartial TBOX of MIA‘s gastronomy ontology
currently covered:• gastronomy• recreationABox (3800 facts)TBox (~ 90 concepts)
currently covered:• gastronomy• recreationABox (3800 facts)TBox (~ 90 concepts)
3.2.2003 - Agentlink I2A 13
MIA: An Information System for Mobile Users
Bernd Thomas
Ontology AgentTBOX:(implies c_mahlzeit c_essen).(equivalent c_speisestaette (and c_ort (some offers c_mahlzeit)
(some of_nationality c_nationalitaet))). (implies c_fastfood (and c_speisestaette (not (some has_service c_service)))).(equivalent c_restaurant (and c_speisestaette (some has_service c_service))).
ABOX:(instance antipasti c_mahlzeit).(instance ristorante c_restaurant).
RA
CE
R system
[eclipse 6]: about(antipasti,X).X = instantiators = ['C_MAHLZEIT'] More? (;)X = instantiators = ['C_ESSEN'] More? (;)X = instantiators = ['C_VERDERBLICH'] More? (;)X = instantiators = ['C_PRODUKT'] More? (;)X = instantiators = ['C_DING'] More? (;)X = instantiators = ['C_FESTSTOFF'] More? (;)[eclipse 7]: related_term(antipasti,X).X = 'OF_NATIONALITY' = 'ITALIENISCH' More? (;)X = 'OFFERED_BY' = 'PIZZERIA' More? (;)
[eclipse 11]: related(antipasti,X).X = pizzeria More? (;)X = osteria More? (;)X = pasticceria More? (;)X = ristorante More? (;)X = rosticceria More? (;)X = trattoria More? (;)X = pizza_zum_mitnehmen More? (;)X = antipasti More? (;)X = carpaccio More? (;)X = cozze More? (;)X = maccaroni More? (;)X = nudeln More? (;)
[eclipse 6]: about(antipasti,X).X = instantiators = ['C_MAHLZEIT'] More? (;)X = instantiators = ['C_ESSEN'] More? (;)X = instantiators = ['C_VERDERBLICH'] More? (;)X = instantiators = ['C_PRODUKT'] More? (;)X = instantiators = ['C_DING'] More? (;)X = instantiators = ['C_FESTSTOFF'] More? (;)[eclipse 7]: related_term(antipasti,X).X = 'OF_NATIONALITY' = 'ITALIENISCH' More? (;)X = 'OFFERED_BY' = 'PIZZERIA' More? (;)
[eclipse 11]: related(antipasti,X).X = pizzeria More? (;)X = osteria More? (;)X = pasticceria More? (;)X = ristorante More? (;)X = rosticceria More? (;)X = trattoria More? (;)X = pizza_zum_mitnehmen More? (;)X = antipasti More? (;)X = carpaccio More? (;)X = cozze More? (;)X = maccaroni More? (;)X = nudeln More? (;)
about(X,Explanation) :-racer('instantiators'(X),Concept),Explanation = ('instantiators'=Concept).
about(X,Explanation) :-racer('concept-ancestors'(X),Subsumers),Explanation = ('concept-ancestors'=Subsumers).
about(X,Explanation) :-racer('concept-descendants'(X),Subsumees),Explanation = ('concept-descendants'=Subsumees).
% meta queriesrelated(X,R):-
racer('retrieve-individual-fillers'(X,'offered_by'),R1),( assert_answer(X,R1,R) ; ( racer('retrieve-individual-fillers'(R1,'offers_same'),R2), assert_answer(X,R2,R) ) ; ( racer('retrieve-individual-fillers'(R1,'inv_offers_same'),R2), assert_answer(X,R2,R) )).
about(X,Explanation) :-racer('instantiators'(X),Concept),Explanation = ('instantiators'=Concept).
about(X,Explanation) :-racer('concept-ancestors'(X),Subsumers),Explanation = ('concept-ancestors'=Subsumers).
about(X,Explanation) :-racer('concept-descendants'(X),Subsumees),Explanation = ('concept-descendants'=Subsumees).
% meta queriesrelated(X,R):-
racer('retrieve-individual-fillers'(X,'offered_by'),R1),( assert_answer(X,R1,R) ; ( racer('retrieve-individual-fillers'(R1,'offers_same'),R2), assert_answer(X,R2,R) ) ; ( racer('retrieve-individual-fillers'(R1,'inv_offers_same'),R2), assert_answer(X,R2,R) )).
On
tolo
gy A
gen
t
queries
info on search topics
3.2.2003 - Agentlink I2A 14
MIA: An Information System for Mobile Users
Bernd Thomas
Outlook
• Need for cooperation with telecom provider for automatic user position estimation via cell information of mobile phones
• Ongoing research in Information Extraction with good results for HTML/XML documents
• Major problem online learning of wrappers, MIA uses very heuristic method ... good ideas needed.
• Ontology based web spidering ... let us see what the semantic web project offers?
• Left out in this project: sharing search and extraction work among agents
3.2.2003 - Agentlink I2A 15
MIA: An Information System for Mobile Users
Bernd Thomas
ReferencesPeter Baumgartner, Ulrich Furbach and Bernd Thomas Model Based Deduction for Knowledge Representation .17. WLP - Workshop Logische Programmierung ,Technische Universität Dresden 4-6. September 2002
Nicholas Kushmerick and Bernd Thomas Adaptive Information Extraction: A Core Technology for Information Agents .In Intelligent Information Agents R&D in Europe: An AgentLink perspective. (2002) Springer.
Gerd Beuster, Bernd Thomas and Christian Wolff Ubiquitous Web Information Agents Workshop on Artificial Intelligence In Mobile Systems ,ECAI'2000 , European Conference on Aritifical Intelligence August 22nd 2000, Berlin,Germany
Bernd Thomas: Token-Templates and Logic Programs for Intelligent Web Search Journal of Intelligent Information Systems , Kluwer Academic Publishers Special Issue: Methodologies for Intelligent Information Systems Volume 14, Number 2/3, March-June 2000, pp. 241-261
Bernd Thomas: Anti-Unification Based Learning of T-Wrappers for Information Extraction Workshop on Machine Learning for Information Extraction ,preceeding Sixteenth National American Conference on Artifical Intelligence (AAAI-99) , July 18-19 Orlando, Florida
3.2.2003 - Agentlink I2A 16
MIA: An Information System for Mobile Users
Bernd Thomas
Register and Profile Creation
• register as new user
• specify search topics according to your individual interests
• a user can create multiple search profiles
• user is not domain bounded
CLICK HERE FOR MOVIE
3.2.2003 - Agentlink I2A 17
MIA: An Information System for Mobile Users
Bernd Thomas
Start Agents and Retrieve Info
• start search for specific city info
• each topic handled by one spider agent
• agent status monitored
• caching of extraction results
• relational extraction results can easily be linked to other web services
CLICK HERE FOR MOVIE
3.2.2003 - Agentlink I2A 18
MIA: An Information System for Mobile Users
Bernd Thomas
Restrict the Search
• search can be restricted by additional keywords
CLICK HERE FOR MOVIE
3.2.2003 - Agentlink I2A 19
MIA: An Information System for Mobile Users
Bernd Thomas
Logout and Comeback Later
• the user can start the search and can come back later to retrieve his information
• this is very helpful for the mobile user to minimize costs
CLICKE HERE FOR MOVIE
3.2.2003 - Agentlink I2A 20
MIA: An Information System for Mobile Users
Bernd Thomas
... coming back
CLICK HERE FOR MOVIE
3.2.2003 - Agentlink I2A 21
MIA: An Information System for Mobile Users
Bernd Thomas
WLS: Learning a Wrapper
• Many web servicesuse highly structered web pages for which machine learning based wrapper techniques are successfully applicable
• WLS is a prototypical web interface to aid the MIA administrator to learn and add new wrappers.
• MIA applies learned wrappers to each page of the associated web domain
CLICK HERE FOR MOVIE
3.2.2003 - Agentlink I2A 22
MIA: An Information System for Mobile Users
Bernd Thomas
WLS: Applying a Wrapper
CLICK HERE FOR MOVIE
3.2.2003 - Agentlink I2A 23
MIA: An Information System for Mobile Users
Bernd Thomas
monitoring the system
CLICK HERE FOR MOVIE