1. The document discusses using ontologies in intelligent tutoring systems to generate personalized web pages for students based on their knowledge level, learning style, and other attributes in their student model. 2. Information is extracted from the web and annotated with metadata. Relevant concepts, facts, and metaphors are identified and dynamically included in generated web pages structured around the domain ontology. 3. This allows the system to continuously update based on new information from the web while ensuring coherence and understanding by reflecting the conceptual structure of the domain for the learner.

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Ontology-Centered
Personalized Presentation of
Intelligent Tutoring Systems
Knowledge Extracted
Knowledge based systems - ontologies
From the Web
Student modeling
Reasoning for:
Student diagnosis
Stefan Trausan-Matu, UPB, ROMANIA Explanations generation
Daniele Maraschi, LIRMM, FRANCE Lesson planning
Stefano Cerri, LIRMM, FRANCE
Intelligent interfaces
Ontologies Ontologies - Concepts
The central part of the domain ontology is a
"An ontology is a specification of a taxonomically organized knowledge base of
conceptualization....That is, an ontology is a concepts:
description (like a formal specification of a Security
program) of the concepts and relationships Bond
that can exist for an agent or a community of Share
agents" (Gruber) OrdinaryShare
PreferenceShare
Stock
Ontologies used in ITSs Student model
Domain Keeps track of the concepts known, unknown or
wrongly known by the student (Dimitrova, Self,
Tutoring
Brna, 2000)
Human-computer interfacing Inferred from results at tests or from interaction
Lexical (visited web pages, topics searched etc.)
Upper Level Is usually defined in relation with the domain
ontology (concept net, Bayesian net)
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Fragment of a learner’s model Personalized web pages
(Dimitrova, Self, Brna, 2000)
Are adapted to each users':
know(ogi,secondary_market,[b_def],u_1_d_2,1). knowledge - ITS student model
know(ogi,negotiated_market,[b_def],u_1_d_2,1).
learning style
not_know(ogi,open_market,[b_def],u_1_d_2,1).
not_know(ogi,primary_market,[b_def],u_1_d_2,1). psychological profile
know(ogi,money_market,[b_def],u_1_d_2,1). goals (e.g. lists of concepts to be learned)
not_know(ogi,primary_market,[a_def],u_1_d_2,2). level (novice, expert)
know(ogi,negotiated_market,[a_def],u_1_d_2,2).
preferences (e.g. style of web pages)
context of interaction
ITS on the Web -
Problems of Browsing for Learning Known ideas
Intelligent search of relevant material
Huge amount of information Knowledge extraction
Permanent appearance of new information XML Metadata
Disorientation Personalization
Adaptive hypermedia
New ideas in our approach Solutions
Permanent updating of information according to The generated web pages include latest
newly published web pages, discovered by information gathered by search agents
agents Use semantic editors for annotation
Assuring the sense of the whole Dynamically generate coherent structures of web
The structure of the web pages should reflect the pages that
conceptual map of the domain – the Ontology reflect the domain ontology,
Facilitation of understanding are filtered according to the learner’s model,
Browsing a holistic, understandable structure may contain latest information,
induce a flow state include metaphors according to intentionality
Use metaphors (especially in CALL)
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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
Phase 2 – From Information to
Phase 1 – Information acquisition Knowledge
WEB
DataBase
Keywords list
XHTML
<?xml version="1.0"?>
<..>
LARFLAST
Inserting
Search keywords
HTML XML
Searching Agent Semantic
models
XML
URLs list
Data Base
Agent collecting data
Database
XHTML
Semantic author
Phase 3 – Knowledge use Metaphor processing for CALL
Client
Web applications server d'application Data
Gathering relevant texts from the web,
Servlet engine TOMCAT
Other
Identification (acquisition) of metaphors
informations
XML
in the selected texts and their XML
MySQL
mark-up of the identified metaphors,
XSL
Personalized usage of the metaphors.
Web browser
eXist JDBC
Native XML
Data base
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Stocks defined in ontologies Metaphors are often used to give
insight in what a concept means
"stock" is AKO "securitiy", "Stocks are very sensitive creatures"
"capital", "asset" or “possession“
“stock” has attributes “owner”, (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.) Other approaches
Holistic character that assure the coherence Adaptive hypermedia (deBra, Brusilovsky,
Houser) local policies like flexible link sorting,
of the presentation, with direct effects on hiding or disabling or by conditionally showing
the learning process – study with Sofia text fragments etc.
University students Planning the content of the presented material
Metaphor identification, annotations, and (Vassilieva; Siekmann, Benzmuller, and all) local
decisions based on the learner model.
usage – intentionality (Trausan 2000) –
other approaches: Lakoff & Johnson, D.
They miss a holistic character!
Fass, J. Martin
Other approaches
The permanent inclusion of new information
gathered and annotated from the web is another
novel feature, not included in other systems.
Existing approaches only provide intelligent
recommendation of interesting web pages,
according to the user profile (Breese, Heckerman,
Kadie; Lieberman) They do not permit the
inclusion of relevant facts in the structure of
ontology-centred structure.
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