• DocumentCode
    680754
  • Title

    From Natural Language Requirements to Formal Specification Using an Ontology

  • Author

    Sadoun, Driss ; Dubois, Clemence ; Ghamri-Doudane, Yacine ; Grau, Brigitte

  • Author_Institution
    LIMSI, Orsay, France
  • fYear
    2013
  • fDate
    4-6 Nov. 2013
  • Firstpage
    755
  • Lastpage
    760
  • Abstract
    In order to check requirement specifications written in natural language, we have chosen to model domain knowledge through an ontology and to formally represent user requirements by its population. Our approach of ontology population focuses on instance property identification from texts. We do so using extraction rules automatically acquired from a training corpus and a bootstrapping terminology. These rules aim at identifying instance property mentions represented by triples of terms, using lexical, syntactic and semantic levels of analysis. They are generated from recurrent syntactic paths between terms denoting instances of concepts and properties. We show how focusing on instance property identification allows us to precisely identify concept instances explicitly or implicitly mentioned in texts.
  • Keywords
    formal specification; natural language processing; ontologies (artificial intelligence); bootstrapping terminology; domain knowledge modeling; extraction rules; formal specification; instance property identification; lexical level; natural language requirements; ontology population; recurrent syntactic paths; requirement specifications; semantic level; syntactic level; terms denoting instances; training corpus; user requirement representation; Context; Ontologies; Semantics; Sociology; Statistics; Syntactics; Terminology; Knowledge representation; Ontology population; Ontology reasoning; Requirement specification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on
  • Conference_Location
    Herndon, VA
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4799-2971-9
  • Type

    conf

  • DOI
    10.1109/ICTAI.2013.116
  • Filename
    6735327