ONTORULE's third presentation at CASCON'11

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. Terminae
Extraction step
– Extract from the acquisition corpus the list of candidate terms using Term
Extractor tools
Normalisation 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. Semex
Rule acquisition
– Rule fragment selection
– Rule transformation
• Revision
– Normalisation of the vocabulary
– Syntax simplification
– Verbalisation of implicit statements
• Decomposition
Rule exploration
- Navigation interface
- SPARQL interface for advanced queries A project funded
by

14. Structure of the candidate rules
A project funded
by
Interlinked SBVR-SE statement
The temperature of the micro_slip_test must be greater than 15 C.

15. Demonstration
A project funded
by