MIA: An Information System for Mobile Users MIA An Information System for Mobile Users Bernd Thomas University Koblenz AI-Research Group

MIA: An Information System for Mobile Users

MIAAn Information System for Mobile Users

Bernd ThomasUniversity KoblenzAI-Research Group

3.2.2003 - Agentlink I2A 2


Bernd Thomas

Outline

Motivation

Architecture & Functionality

Agents, Communication & Distribution

Information Extraction

Using Ontological Knowledge



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.



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



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



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



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:



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



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



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



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)



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)



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



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



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



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



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




Bernd Thomas

Restrict the Search

• search can be restricted by additional keywords




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



Bernd Thomas

... coming back




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




Bernd Thomas

WLS: Applying a Wrapper




Bernd Thomas

monitoring the system


Documents

MIA: An Information System for Mobile Users MIA An Information System for Mobile Users Bernd Thomas University Koblenz AI-Research Group