Crosslingual Retrieval in an eLearning Environment Cristina Vertan, Kiril Simov, Petya Osenova, Lothar Lemnitzer, Alex Killing, Diane Evans, Paola Monachesi Artificial Intelligence and Human-Oriented Computing (September 10-13, Roma, Italy)

Framework EU-Project LT4eL: Language Technology for eLearning ( 12 Partnerswww.lt4el.eu Learning objects: in 9 Languages eLearning Test-System : open source platform ILIAS ( Domain: Computer Science for non CS specialists Services: Keyword Extraction, Definition Detection, Semantic Search

Architecture (2) Place of multilinguality - real situation 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

Processing of the keywords in LOs Formalization of the meanings – definitions from Internet Linking to an upper ontology (DOLCE) Addition of new concepts Addition of relations Documentation Lexicons in 9 languages The Creation of LT4eL Ontology

Connection with other Ontologies DOLCE (Guarino&al.) WordNet LT4EL

A document (file) connected to the World Wide Web and viewable by anyone connected to the internet who has a web browser. Hyper CSnCS: Equal WN20: ENG n ID: id1757 Ontology Example

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

Ontology and Multilingual Data EN DE DT Lexicons Documents Ontology DT DE EN

Starting points –A multilingual document collection –An ontology including a domain ontology on the domain of the documents –Concept lexicalisations in various languages –Annotation of concepts in the documents Multilingual, Semantic Document Retrieval

Improved access to documents –Find docs that would not be found by simple text search Multilinguality –One implementation for multiple languages Crosslinguality –Retrieve documents in languages other than language of the query or ontology presentation Goals of the Approach

The User 1.Submits a free text query 2.Sees document list A list of documents is displayed with some meta information, for example: title; length; original language; keywords and concepts that are common to both the query and the document; other keywords and concepts that are related to the document but not to the query 3.Sees concepts for refining search Concepts related to the search query  starting point for browsing No related concepts from search query  root of ontology starting point Outline of Search Procedure

4.Views documents User looks into the documents from the list and estimates their relevance 5.Browses ontology Entry point depends on the initial query 6.Selects concepts Concepts are used for query refinement 7.Selects search option The search option is about how to use the ontology fragments for search Outline of Search Procedure

8.Sees new document list A new list of documents is displayed, based only on ontological search 9.Sees updated concept browsing units Concepts that are common to the found documents Example: Concept “Report”  Some documents about academic writing  Concept “Publication” 10.Repeats steps from step 5 (Browse ontology) User selects another set of related concepts and submits it as the search key, etc Outline of Search Procedure

Integration in ILIAS

We are ready to Compare different searches over LOs Tune parameters of the searches Validate the user added value of the provides services Add relations and extend the search Provide context of the result from the search Conclusion and Future Work