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Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
Cascon2011_3_terminae+semex
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Cascon2011_3_terminae+semex

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ONTORULE's third presentation at CASCON'11

ONTORULE's third presentation at CASCON'11

Published in: Technology, Education
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  • 1. Text-based knowledge acquisition tools Abdoulaye Guissé, Adeline Nazarenko, François Lévy, Nouha Omrane, Sylvie Szulman (Paris13)
  • 2. Why starting with texts? Domain knowledge cannot be fully automatically extracted from texts Texts are nevertheless useful  Texts are available data (≠ experts)  Texts partly reflect the domain conceptualisation (TBox)  Texts may contain pieces of factual knowledge (ABox)  Policy documents express business rules  It is often important to trace knowledge to textual sources Natural Language Processing in ONTORULE  Acquiring knowledge from written policies  Enriching NLP tools with SBVR-based functionalities (metamodel and SE)  Integrating policy documents into the management system
  • 3. Text-based knowledge acquisition tools• Terminae Interactive acquisition of domain ontological knowledge (conceptual vocabulary including concepts, concept definitions, roles and some instances)• Semex Combination of information extraction techniques and manual modelling for the acquisition of rules expressed in terms of the conceptual vocabulary
  • 4. Overall acquisition approach
  • 5. Text-based ontology acquisition Terminae
  • 6. Using Terminae for ontology acquisition
  • 7. Building Lexicalized ontology from texts• Goals – Building a domain ontology – Documentation • Traceability to source documents – Semantic annotation of source documents • Query the text
  • 8. TerminaeExtraction step– Extract from the acquisition corpus the list of candidate terms using Term Extractor toolsNormalisation step– Filter and select relevant meanings of ambigious terms (clustering terms) (i.e. member: airline participant/ customer)– Create and structure termino-concepts (relevant and disambiguated terms of the domain)Formalisation step- Create concepts and instances linked to each termino-concept
  • 9. Rule edition and navigation Semex
  • 10. Integrating policy documents in BRMSs• Goals – Rule Acquisition – Documentation • Traceability to source documents • Verbalisation and presentation of internal knowledge – Maintenance • Consistency checking • Policy evolution A project funded by
  • 11. Underlying Index structure A project funded by
  • 12. Editing rules: an example A project funded by
  • 13. SemexRule acquisition– Rule fragment selection– Rule transformation • Revision – Normalisation of the vocabulary – Syntax simplification – Verbalisation of implicit statements • DecompositionRule exploration- Navigation interface- SPARQL interface for advanced queries A project funded by
  • 14. Structure of the candidate rules A project funded byInterlinked SBVR-SE statementThe temperature of the micro_slip_test must be greater than 15 C.
  • 15. Demonstration A project funded by

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