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Transcript of Larflast
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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.