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LT4EL - Integrating Language Technology and Semantic Web techniques in eLearning

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Title: LT4EL - Integrating Language Technology and Semantic Web techniques in eLearning


1
LT4EL - Integrating Language Technology and
Semantic Web techniques in eLearning
  • Lothar Lemnitzer
  • GLDV AK eLearning, 11. September 2007

2
LT4eL - Language Technology for eLearning
  • Start date 1 December 2005
  • Duration 30 months
  • Partners 12
  • EU finacing 1.5 milion Euro
  • Type project STREP IST 027391

3
LT4eL - Partners
  • Utrecht University, The Netherlands (coordinator)
  • University of Hamburg, Germany
  • University Al.I.Cuza of Iasi, Romania
  • University of Lisbon, Portugal
  • Charles University Prague, Czech Republic
  • IPP, Bulgarian Academy of Sciences, Bulgaria
  • University of Tübingen, Germany
  • ICS, Polish Academy of Sciences, Poland
  • Zürich University of Applied Sciences
    Winterthur, Switzerland
  • University of Malta, Malta
  • Eidgenössische Hochschule Zürich
  • Open University, United Kingdom

4
LT4eL- Objectives -1-
  • Scientific and Technological Objectives
  • Integration of language technology resources and
    tools in eLearning
  • Integration of semantic Knowledge in eLearning
  • Improve (multilingual) retrieval of learning
    material

5
LT4eL - Languages
  • Bulgarian
  • Czech
  • Dutch
  • German
  • Maltese
  • Polish
  • Portuguese
  • Romanian
  • English

6
LT4eL- Objectives -2-
  • Political objectives
  • Support multilinguality
  • Knowledge transfer
  • Awareness raising
  • Exploitation of resources
  • Facilitate access to education

7
Tasks
  • Creation of an archive of learning objects
  • Semi-automatic metadata generation driven by NLP
    tools
  • Keyword extractor
  • Definition extractor
  • Enhancing eLearning with semantic knowledge
  • ontologies
  • Integration of functionalities in the ILIAS
    Learning Management System
  • Validation of new functionalities in the ILIAS
    Learning Management System
  • Address Multilinguality

8
Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
9
Creation of a learning objects archive
  • collection of the learning material (uploads
    updates at http//consilr.info.uaic.ro/uploads_lt4
    el/ - passwd protected)
  • IST domains for the LOs
  • 1. Use of computers in education, with
    sub-domains
  • 1.1 Teaching academic skills, with sub-domains
  • 1.1.1 Academic skills
  • 1.1.2 Relevant computer skills for the
    above tasks (MS Word, Excel, Power Point, LaTex,
    Web pages, XML)
  • 1.1.3 Basic computer skills (use of
    computer for beginners) (chats, e-mail, Intenet)
  • 1.2 Impact of e-Learning on education
  • 2. Calimera documents (parallel corpus developed
    in the Calimera FP5 project, http//www.calimera.o
    rg/ )

10
Collection of learning materials and linguistic
tools
  • normalization of the learning material
  • convertors from html/txt to basic XML format
  • Inventarization and classification of existing
    tools (http//consilr.info.uaic.ro/uploads_lt4el/t
    ools/all.php?) relevant to
  • the integration of language technology resources
    in eLearning
  • the integration of semantic knowledge
  • Inventarization and classification of existing
    language resources corpora and frequencies
    lists http//consilr.info.uaic.ro/uploads_lt4el/m
    enu/all.php
  • lexica http//www.let.uu.nl/lt4el/wiki/index.php/
    Lexica_Joint_Table

11
Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
12
Semi-automatic metadata generation with LT and NLP
  • Aims
  • supporting authors in the generation of metadata
    for LOs
  • improving keyword-driven search for LOs
  • supporting the development of glossaries for
    learning material

13
Metadata
  • metadata is essential to make LOs visible for
    larger groups of users
  • authors are reluctant or not experienced enough
    to supply it
  • NLP tools will help them in that task
  • the project uses the LOM metadata schema as a
    blueprint

14
Identification of keywords
  • Good keywords have a typical, non random
    distribution in and across LOs
  • Keywords tend to appear more often at certain
    places in texts (headings etc.)
  • Keywords are often highlighted / emphasised by
    authors

15
Modelling Keywordiness
  • Residual Inverse document frequency used to model
    inter text distribution of KW
  • Term burstiness used to model intra text
    distribution of KW
  • Knowledge of text structure used to identify
    salient regions (e,g, headings)
  • Layout features of texts used to identify
    emphasised words and weight them higher

16
Challenges
  • Treating multi word keywords (suffix arrays will
    be used to identify n-grams of arbitrary length)
  • Assigning a combined weight which takes into
    account all the aforementioned factors
  • Multilinguality

17
Evaluation
  • Manually assigned keywords will be used to
    measure precision and recall of key word
    extractor
  • Human annotator to judge results from extractor
    and rate them

18
Identification of definitory contexts
  • Empirical approach based on linguistic annotation
    of LO
  • Identification of definitory contexts is language
    specific
  • Workflow
  • Definitory contexts are searched and marked in
    LOs (manually)
  • Local grammars are drafted on the basis of these
    examples
  • Linguistic annotation is used for these grammars
  • Grammars are applied to new LOs
  • Extraction of definitory context performed by
    Lxtransduce (University of Edinburgh - LTG)

19
Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
20
Ontology-based cross-lingual retrieval
  • Metadata can also be represented by ontologies
  • Creation of a domain ontology in the area of LOs
  • For consistency reasons we employ also an upper
    ontology (DOLCE)
  • Lexical material in all 9 languages is mapped on
    the ontology and on the upper ontology
  • Ontology will allow for multilingual retrieval of
    LOs

21
Domain Ontology creation
  • ? lexicon (vocabulary with natural language
    definitions)
  • simple taxonomy
  • ? thesaurus (taxonomy plus related-terms)
  • relational model (unconstrained use of arbitrary
    relations)
  • fully axiomatized theory

22
Domain Ontology
  • terminological dictionary in chosen domain
  • - term in English,
  • - a short definition in English
  • - translation of the term
  • formalize the definitions to reflect the
    relations like is-a, part-of, used-for
  • definitions translated in OWL-DL
  • not achieve a fully axiomatized theory, but
    relational model of the domain
  • connection to the upper ontology will enforce
    the inheritance of the axiomatization of the
    upper ontology to the concepts in the domain
    ontology

23
Upper Ontology DOLCE
  • the ontology should be constructed on rigorous
    basis
  • it should be easy to be represented as an
    ontological language such as RDF or OWL
  • there are domain ontologies constructed with
    respect to it
  • it can be related to lexicons - either by
    definition, or by already existing mapping to
    some lexical resource

24
Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
25
Integration in ILIAS
  • Integration of LT4eL functionalities for
    semi-automated metadata generation, definitory
    context extraction and ontology supported
    extended data retrieval into a learning
    management system (prototype based on ILIAS LMS)
  • Developing and providing documentation for a
    standard-technology-based interface between the
    language technology tools and learning management
    systems

26
Integration of functionalities
Development Server (CVS)
Content Portal
KW/DC
Ontology
ILIAS
LOs
Code
Code/Data
Code
Migration Tool
Nightly Updates
Use functionalities through SOAP
ILIAS Server
Java Webserver (Tomcat)
Application Logic
Webservices
nuSoap
LOs
Axis
KW/DC/Onto Java Classes / Data
Evaluate functionalities in ILIAS
Third Party Tools
User Interface
Servlets/JSP
Evaluate functionalities directly
27
Validation of enhanced LMS.
  • Challenge is to answer these questions
  • How does this compare with what can already be
    done with existing systems?
  • What added value is there?
  • What is the educational / pedagogic value of
    these functionalities?
  • Problem is to evaluate the functionality and
    separate from issues of usability or
    unfamiliarity with the LMS platform.How can we
    expect users to identify any benefit?

28
How can we expect users to identify any benefit?
  • Present them with tasks to complete using LMS
  • With no project functionality
  • With project functionality
  • Partial
  • Full
  • Identify potential users
  • Course Creators
  • Content Authors or Providers
  • Teachers
  • Students
  • studying in their own language
  • studying in a second language

29
Create outline User Scenarios
  • We define scenarios, in this context, as
  • a story focused on a user or group of users
    which provides information on
  • the nature of the users,
  • the goals they wish to achieve and
  • the context in which the activities will take
    place.
  • They are written in ordinary language, and are
    therefore understandable to various stakeholders,
    including users.
  • They may also contain different degrees of detail.

30
Conclusions
  • Improve retrieval of learning material
  • Facilitate construction of user specific courses
  • Improve creation of personalized content
  • Support decentralization of content management
  • Allow for multilingual retrieval of content

31
Contact
  • www.lt4el.eu
  • Contact for information Paola.Monachesi_at_let.uu.nl
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