Cascon2011_3_terminae+semex

Text-based knowledge acquisition tools Abdoulaye Guissé, Adeline Nazarenko, François Lévy, Nouha Omrane, Sylvie Szulman (Paris13)

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

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

Overall acquisition approach

Text-based ontology acquisition Terminae

Using Terminae for ontology acquisition

Building Lexicalized ontology from texts• Goals – Building a domain ontology – Documentation • Traceability to source documents – Semantic annotation of source documents • Query the text

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

Rule edition and navigation Semex

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

Underlying Index structure A project funded by

Editing rules: an example A project funded by

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

Structure of the candidate rules A project funded byInterlinked SBVR-SE statementThe temperature of the micro_slip_test must be greater than 15 C.

Demonstration A project funded by

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Cascon2011_3_terminae+semex

by ONTORULE Project on Dec 09, 2011

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Cascon2011_3_terminae+semex — Presentation Transcript