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Multilingual Extraction Ontologies

Multilingual Extraction Ontologies. Outline. Our MEG A possible WWW paper Getting there from here What we propose(d) to do Multilingual resources Evaluation. MEG details. Funding S tarts ASAP Stops at end 2011 PI’s: Embley, Liddle, Lonsdale, Tijerino

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Multilingual Extraction Ontologies

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  1. Multilingual Extraction Ontologies

  2. Outline • Our MEG • A possible WWW paper • Getting there from here • What we propose(d) to do • Multilingual resources • Evaluation

  3. MEG details • Funding • Starts ASAP • Stops at end 2011 • PI’s: Embley, Liddle, Lonsdale, Tijerino • $20,000 total: $18,000 for student wages, $1500 for travel, $500 for supplies (mobile device)

  4. MEG objective(s) • Enhance ontologies: • Compound recognizers • Pattern discovery • Discover and extract relationships among objects • Discover patterns that can lead to identification and extraction of object instances and relationship instances

  5. MEG objective(s) • Demonstrate crosslinguistic viability of ontologies • Create crosslinguistic mappings • Integrate lexicons for multilingual processing • Develop multilingual (crosslingual?) value recognizers

  6. MEG objective(s) • Tech transfer • Develop working prototype showing multilingual capabilities • Hand-held travel assistant • Build business plan, enter BYU competition • Develop patent application

  7. Research plan • Winter 2010: recruit students • CS undergrad • Linguistics undergrad • e-business undergrad • Activities • Setup: Eclipse, OntoES, repository

  8. Premises • English Web is increasingly being overshadowed • We want to show viability of our approach crosslinguistically • Some efforts exist: Norwegian drilling, VerbMobil, EU trains, CLEF, NTCIR • Not all use ontologies

  9. Approach • Declare a narrow domain ontology (cf. car ads) • Add linguistic recognizers (data frames ++) • Extend to (an)other language(s) • Let ontological content be a sort of “interlingua”

  10. Japanese extraction ontology

  11. Multilingual adaptation • OntoES, workbench should be inherently capable • UTF-8, Java • Some work remains • Knowledge sources • Many exist; don’t have resources to re-invent the wheel • WordNet, termbases

  12. CS-related work • New algorithms, data structures for linguistically-grounded intologies • Implement compound recognizers • Design and run evaluation

  13. Linguistics-related work • Locate and evaluate lexical resources • Engineer ways to implement multiple or crosslinguistic language resources • Help in system evaluation

  14. Business-related work • Research needs of international travelers • Brainstorm business app, do market research • Write, submit business plan • Investigate tech transfer, patent issues

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