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Ontology-Centered Personalized Presentation of Knowledge ExtractedFrom the Web

Stefan Trausan-Matu, UPB, ROMANIADaniele Maraschi, LIRMM, FRANCEStefano Cerri, LIRMM, FRANCE

Intelligent Tutoring Systems� Knowledge based systems - ontologies

� Student modeling

� Reasoning for:� Student diagnosis

� Explanations generation

� Lesson planning

� Intelligent interfaces

Ontologies

"An ontology is a specification of a conceptualization....That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents" (Gruber)

Ontologies - Concepts

The central part of the domain ontology is a taxonomically organized knowledge base of

concepts:

Security

Bond

Share

OrdinaryShare

PreferenceShare

Stock

Ontologies used in ITSs� Domain

� Tutoring

� Human-computer interfacing

� Lexical

� Upper Level

Student model

� Keeps track of the concepts known, unknown or wrongly known by the student (Dimitrova, Self, Brna, 2000)

� Inferred from results at tests or from interaction (visited web pages, topics searched etc.)

� Is usually defined in relation with the domain ontology (concept net, Bayesian net)

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know(ogi,secondary_market,[b_def],u_1_d_2,1).know(ogi,negotiated_market,[b_def],u_1_d_2,1).not_know(ogi,open_market,[b_def],u_1_d_2,1).not_know(ogi,primary_market,[b_def],u_1_d_2,1).know(ogi,money_market,[b_def],u_1_d_2,1).not_know(ogi,primary_market,[a_def],u_1_d_2,2).know(ogi,negotiated_market,[a_def],u_1_d_2,2).

Fragment of a learner’s model (Dimitrova, Self, Brna, 2000)

Personalized web pages

Are adapted to each users':� knowledge - ITS student model� learning style� psychological profile� goals (e.g. lists of concepts to be learned) � level (novice, expert)� preferences (e.g. style of web pages)� context of interaction

ITS on the Web -Problems of Browsing for Learning

� Huge amount of information

� Permanent appearance of new information

� Disorientation

Known ideas

� Intelligent search of relevant material

� Knowledge extraction

� XML Metadata

� Personalization

� Adaptive hypermedia

New ideas in our approach� Permanent updating of information according to

newly published web pages, discovered by agents

� Assuring the sense of the whole� The structure of the web pages should reflect the

conceptual map of the domain – the Ontology� Facilitation of understanding� Browsing a holistic, understandable structure may

induce a flow state� Use metaphors (especially in CALL)

Solutions� The generated web pages include latest

information gathered by search agents� Use semantic editors for annotation� Dynamically generate coherent structures of web

pages that� reflect the domain ontology,� are filtered according to the learner’s model,� contain latest information,� include metaphors according to intentionality

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LARFLAST(LeARning Foreign Language Scientific Terminology COPERNICUS EU project)

• Leeds University – UK,• Montpellier University - France,• RACAI – Romania,• Manchester University - UK,• Sofia University - Bulgaria,• Sinferopol University - Ukraine

Objective: To provide a set of tools, available on the web, for supporting the learning of foreign terminology in finance

WEB

Inserting

Search keywords

Searching Agent

Agent collecting data

Keywords list

<?xml version="1.0"?>

<..>

URLs list

Database

Phase 1 – Information acquisitionDataBase

XHTML

Semantic author

XMLHTMLSemantic

modelsXML

Data Base

XHTML

LARFLAST

Phase 2 – From Information to Knowledge

MySQL

Web browser

Servlet engine TOMCAT

Native XMLData base

XML

XSL

Otherinformations

eXist JDBC

Client Web applications server d'application Data

Phase 3 – Knowledge use Metaphor processing for CALL

� Gathering relevant texts from the web,� Identification (acquisition) of metaphors

in the selected texts and their XML mark-up of the identified metaphors,

� Personalized usage of the metaphors.

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Stocks defined in ontologies

� "stock" is AKO "securitiy", "capital", "asset" or “possession“

� “stock” has attributes “owner”, …

Metaphors are often used to give insight in what a concept means

"Stocks are very sensitive creatures"

(New York Stock Exchange web page http://www.nyse.com/).

Semantic editing (Trausan, 2000)

LARFLAST

Dynamic generation of personalized web pages

� Runs from an Apache servlet� Adapts to the learner’s model, transferred from

another web site� Parameterized, easy to configure for new patterns

of web pages and structures� Includes relevant metaphors and texts from a

corpus

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Conclusions� Serenditipous search, annotation, and use of

information

� The domain ontology used for:� serendipitous search

� XML semantic annotation

� retrieval of relevant metaphors

� structuring the dynamically generated web pages

� including knowledge in the web pages

Conclusions (cont.)� Holistic character that assure the coherence

of the presentation, with direct effects on the learning process – study with Sofia University students

� Metaphor identification, annotations, and usage – intentionality (Trausan 2000) –other approaches: Lakoff & Johnson, D. Fass, J. Martin

Other approaches� Adaptive hypermedia (deBra, Brusilovsky,

Houser) local policies like flexible link sorting,hiding or disabling or by conditionally showingtext fragments etc.

� Planning the content of the presented material(Vassilieva; Siekmann, Benzmuller, and all) localdecisions based on the learner model.

They miss a holistic character!

Other approaches � The permanent inclusion of new information

gathered and annotated from the web is anothernovel feature, not included in other systems.

� Existing approaches only provide intelligentrecommendation of interesting web pages,according to the user profile (Breese, Heckerman,Kadie; Lieberman) They do not permit theinclusion of relevant facts in the structure ofontology-centred structure.