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Using a domain-ontology and semantic search in an eLearning environment Lothar Lemnitzer, Kiril Simov, Petya Osenova, Eelco Mossel and Paola Monachesi International Conference on Engineering Education, Instructional Technology, Assessment, and E-learning (EIAE 07), December 2007

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using a domain ontology and semantic search in an elearning environment

Using a domain-ontology and semantic search in an eLearning environment

Lothar Lemnitzer, Kiril Simov, Petya Osenova, Eelco Mossel and Paola Monachesi

International Conference on Engineering Education, Instructional Technology, Assessment, and E-learning(EIAE 07), December 2007

outline of the talk
Outline of the Talk
  • Introductory notes
  • LT4eL Domain Ontology
  • Ontology-based Lexicon Model
  • Semantic annotation of learning objects
  • Semantic Search
  • Evaluation
  • Conclusions
introductory notes 1
Introductory notes (1)
  • LT4eL European project aims at demonstrating the relevance of language technology and ontologies for improving learning management systems (LMS)
  • Multilingual approach
slide4

LMS

User Profile

LING. PROCESSOR

EN

GE

Lemmatizer, POS, Partial Parser

Ontology

CROSSLINGUAL RETRIEVAL

Lexikon

Lexikon

Lexicon

Lexikon

Lexicon

Lexikon

Lexikon

Lexikon

Lexikon

RO

PT

PL

CZ

BG

DT

MT

PT

GE

PL

RO

DT

MT

EN

CZ

Documents SCORM

Pseudo-Struct.

Basic XML

CONVERTOR 2

Documents SCORM

Documents HTML

Pseudo-Struct

Glossary

CONVERTOR 1

Metadata (Keywords)

Ling. Annot XML

BG

EN

Documents User

(PDF, DOC, HTML,

SCORM,XML)

REPOSITORY

introductory notes 2
Introductory notes (2)

We created and use

  • A domain ontology
  • Lexicons for several languages
  • (Linguistically, semantically) annotated learning objects

for semantic search

lt4el domain ontology general issues
LT4eL Domain Ontology: general issues
  • The domain: Computer Science for Non-Computer Scientists
  • The role of the ontology: indexing of the Los, semantic search
lt4el domain ontology creation
LT4eL Domain Ontology: creation

BG

Keywords

annotation

Translation

into EN

EN

PT

Definition

Collection

CZ

Concept

creation

NL

RO

MT

PO

current state of the ontology
Current state of the ontology
  • about 750 domain concepts,
  • about 50 concepts from DOLCE
  • about 250 intermediate concepts from OntoWordNet
  • about 200 new concepts extracted from LOs
ontology based lexicon model 1
Ontology-Based Lexicon Model (1)
  • The lexicons represent the main interface between the user\'s query and the ontology
  • Lexicons for all languages of the project have been created
ontology based lexicon model 2
Ontology-Based Lexicon Model (2)
  • all the important concepts within a domain should be included
  • we allow the lexicons to contain also non-lexicalized phrases (e.g. mapping variety)
example from the dutch lexicon
Example from the Dutch lexicon

<entryid="id60">

<owl:Classrdf:about="lt4el:BarWithButtons">

<rdfs:subClassOf>

<owl:Classrdf:about="lt4el:Window"/>

</rdfs:subClassOf>

</owl:Class>

<def>A horizontal or vertical bar as a part of a window,

that contains buttons, icons.</def>

<termglang="nl">

<termshead="1">werkbalk</term>

<term>balk</term>

<termtype="nonlex">balk met knoppen</term>

<term>menubalk</term>

</termg>

</entry>

semantic annotation of learning objects
Semantic Annotation of Learning Objects
  • Within the project we performed both types of annotation,:
    • inline
    • through metadata
  • The inline annotation will be used:
    • as a mechanism to validate the coverage of the ontology;
    • for semantic retrieval
semantic search
Semantic Search

Aims at improved retrieval of documents

  • Find documents that would not be found by simple full text search; e.g. search for “screen” retrieves documents that contain “monitor”

Crosslingual

  • Find documents in languages different from search/interface language;
  • Advantage: No need to translate search query
slide14

Search procedure

Search-Term(s)

Search-Concepts

Retrieved Documents

Lexicons:contain term-concept mappings

Ontology: contains concepts

DocumentDatabase

select concepts

Visualisation

search procedure
Search procedure
  • Provide a search query in Language L(1)
  • Find terms in lexicons of L(1) that reflect search query
  • Find relevant documents for concepts in L(1), L(2) etc.
  • Rank for set of found documents
  • Create ontology fragment containing necessary information to present concept neighbourhood
evaluation of semantic search
Evaluation of Semantic Search

Aspects:

  • Does semantic search return correct results, i.e. appropriate documents?
  • How easy is it to use semantic search?
  • Are the results better (precision/recall) than with keyword search or full text search?
  • Does semantic search improve learning processes?
formal evaluation
Formal Evaluation

Procedure: Search for paragraphs with query

formed on the basis of Concepts from ontology

#Program* + #Slide

formed on the basis of Terms in the lexicons

Program, Software, Editor, Slide

For a variety of languages.

conclusions21
Conclusions
  • Evaluation experiment showed the superiority of semantic search over simple full text search
  • Our architecture introduces cross-lingual search into the learning process
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