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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

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

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  1. 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

  2. Outline of the Talk • Introductory notes • LT4eL Domain Ontology • Ontology-based Lexicon Model • Semantic annotation of learning objects • Semantic Search • Evaluation • Conclusions

  3. Introductory notes (1) • LT4eL European project aims at demonstrating the relevance of language technology and ontologies for improving learning management systems (LMS) • Multilingual approach

  4. 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

  5. Introductory notes (2) We created and use • A domain ontology • Lexicons for several languages • (Linguistically, semantically) annotated learning objects for semantic search

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

  7. LT4eL Domain Ontology: creation BG Keywords annotation Translation into EN EN PT Definition Collection CZ Concept creation NL RO MT PO

  8. 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

  9. 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

  10. 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)

  11. 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>

  12. 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

  13. 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

  14. Search procedure Search-Term(s) Search-Concepts Retrieved Documents Lexicons:contain term-concept mappings Ontology: contains concepts DocumentDatabase select concepts Visualisation

  15. 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

  16. Search with ILIAS

  17. 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?

  18. 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.

  19. Conclusions

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

  21. Contact • www.lt4el.eu • Contact for information: Paola.Monachesi@let.uu.nl

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