Using a domain-ontology and semantic search in an eLearning environment Lothar Lemnitzer, Kiril Simov, Petya Osenova, Eelco Mossel and Paola Monachesi.

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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

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) LT4eL European project aims at demonstrating the relevance of language technology and ontologies for improving learning management systems (LMS) Multilingual approach

Lexikon CZ EN CONVERTOR 1 Documents SCORM Pseudo-Struct. Basic XML LING. PROCESSOR Lemmatizer, POS, Partial Parser CROSSLINGUAL RETRIEVAL LMS User Profile Documents SCORM Pseudo-Struct Metadata (Keywords) Ling. Annot XML Ontology CONVERTOR 2 Documents HTML Lexikon PT Lexikon RO Lexikon PL Lexicon GE Lexikon MT Lexikon BG Lexikon DT Lexicon EN PLGE BG PTMTDTRO EN Documents User (PDF, DOC, HTML, SCORM,XML) REPOSITORY Glossary

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 The domain: Computer Science for Non-Computer Scientists The role of the ontology: indexing of the Los, semantic search

LT4eL Domain Ontology: creation Keywords annotation BG EN PT NL MT CZ PO RO Translation into EN Definitio n Collectio n Concept creation

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) 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) 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 A horizontal or vertical bar as a part of a window, that contains buttons, icons. werkbalk balk balk met knoppen menubalk

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 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

Ontology: contains concepts Document Database Lexicons: contain term-concept mappings Visualisation select concepts Search-Term(s) Search-Concepts Retrieved Documents 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

Search with ILIAS

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 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.

Conclusions LanguageFull-text search (F-measure) Semantic search (F- measure) Bulgarian56,2591,30 Dutch47,5094,12 English27,9679,42 German36, Polish12,5050,00 Portuguese28,6733,33

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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