Download PDF
Methods Inf Med 1995; 34(01/02): 15-24
DOI: 10.1055/s-0038-1634577
Original article
Schattauer GmbH

Issues in the Structuring and Acquisition of an Ontology for Medical Language Understanding

P. Zweigenbaum
1   DIAM – SIM, Service d’Informatique Médicale, Assistance Publique – Hôpitaux de Paris
,
B. Bachimont
1   DIAM – SIM, Service d’Informatique Médicale, Assistance Publique – Hôpitaux de Paris
,
J. Bouaud
1   DIAM – SIM, Service d’Informatique Médicale, Assistance Publique – Hôpitaux de Paris
,
J. Charlet
1   DIAM – SIM, Service d’Informatique Médicale, Assistance Publique – Hôpitaux de Paris
,
J.-F. Boisvieux
1   DIAM – SIM, Service d’Informatique Médicale, Assistance Publique – Hôpitaux de Paris
› Author AffiliationsThis work has been partly supported by the European Community project MENELAS (AIM 2023)
Further Information

Publication History

Publication Date:
09 February 2018 (online)

    Permissions and Reprints

Abstract:

Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge-acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. We examined some methodological and theoretical principles to enforce this conformity. These principles result from our experience in MENELAS, a medical language understanding project.

Keywords:

Natural Language Understanding - Ontology - Knowledge Acquisition - Knowledge Representation - Conceptual Graphs
 

  • References

  • 1 Sager N, Lyman M, Bucknall C, Nhan NT, Tick LJ. The language processing and the representation of clinical data. J Am Med Informatics Assoc 1994; 1: 142-60.
    CrossrefPubMedGoogle Scholar
  • 2 Sager N, Lyman M, Nhan NT, Tick LJ. Medical language processing: Applications to patient data representation and automatic encoding. Meth Inform Med 1995; 34: 140-6.
    PubMedGoogle Scholar
  • 3 Baud R, Rassinoux AM, Scherrer J-R. Natural language processing and seman tical representation of medical texts. Meth Inform Med 1992; 31: 117-25.
    PubMedGoogle Scholar
  • 4 Schröder M. Knowledge-based processing of medical language: A language engineering approach. In: Proceedings of GWAI’92. Bonn: September 1992: 190-9.
    Google Scholar
  • 5 Friedman C, Alderson PO, Austin JH, Cimino JJ, Johnson SB. A general naturallanguage text processor for clinical radiology. J Am Med Informatics Assoc March 1994; 1: 161-74.
    CrossrefPubMedGoogle Scholar
  • 6 Cavazza M, Zweigenbaum P. A semantic analyzer for natural language understanding in an expert domain. App Artif Intell 1994; 8: 425-53.
    PubMedGoogle Scholar
  • 7 Zweigenbaum P. Consortium MENELAS. MENELAS: an access system for medical records using natural language. Comput Meth Prog Biomed 1994; 45: 117-20.
    CrossrefPubMedGoogle Scholar
  • 8 Newell A. The knowledge level. Artif Intell 1982; 18: 87-127.
    CrossrefPubMedGoogle Scholar
  • 9 Sowa JF. ed. Principles of Semantic Networks. San Mateo Ca: Morgan Kaufman; 1991
    Google Scholar
  • 10 Schank RC. ed. Conceptual Information Processing. Amsterdam: North-Holland Publ; 1975
    Google Scholar
  • 11 Brachman RJ, McGuinness DL, Patel-Schneider PF, Resnick LA, Borgida A. Living with CLASSIC: when and how to use a KL-ONE-Like language. In: Sowa, ed. Principles of Semantic Networks. San Mateo Ca: M. Kaufman; 1991: 401-56.
    Google Scholar
  • 12 Sowa JF. Conceptual Structures: Information Processing in Mind and Machine. London: Addison-Wesley; 1984
    Google Scholar
  • 13 Sowa JF. Conceptual analysis as a basis for knowledge representation. Meth Inform Med 1995; 34: 165-71.
    PubMedGoogle Scholar
  • 14 Baud RH, Rassinouy AM, Wagner JC. et al. Representing clinical narratives by Sowa’s Conceptual Graphs. Meth Inform Med 1995; 34: 176-86.
    PubMedGoogle Scholar
  • 15 Campbell K, Musen M. Representation of clinical data using SNOMED III and conceptual graphs. In: Proc Sixteenth Ann Symp Computer Applications in Medical Care. New York: McGraw Hill; 1992: 354-8.
    Google Scholar
  • 16 Sabah G, Vilnat A. Flexible case structure implemented into a deterministic parser. In: Way EC. ed. Sixth Annual Workshop on Conceptual Graphs. New York: Binghamton; 1991: 343-57.
    Google Scholar
  • 17 McCray AT. The UMLS semantic network. In: Proc Thirteenth Ann Symp Computer Applications in Medical Care. New York: IEEE Comp Soc; 1989: 503-7.
    Google Scholar
  • 18 Cimino JJ, Clayton PD, Hripcsak G, Johnson SB. Knowledge-based approaches to the maintenance of a large controlled medical terminology. J Am Med Informatics Assoc 1994; 1: 35-50.
    CrossrefPubMedGoogle Scholar
  • 19 Volot F, Zweigenbaum P, Bachimont B. et al. Structuration and acquisition of medical knowledge: Using UMLS in the Conceptual Graph formalism. In: Proc Seventeenth Ann Symp Computer Applications in Medical Care. New York: McGraw Hill; 1993: 710-4.
    Google Scholar
  • 20 Rector AL, Gangemi A, Galeazzi E, Glowinski AE, Rossi-Mori A. The GALEN CORE model schemata for anatomy: Towards a re-usable application-independent model of medical concepts. In: Proc MIE 94. Lisbon: 1994: 229-33.
    Google Scholar
  • 21 Carenini G, Moore JD. Using the UMLS semantic network as a basis for constructing a terminological knowledge base: A preliminary report. In: Proc Seventeenth Ann Symp Computer Applicatons in Medical Care. New York: McGraw Hill; 1993: 725-9.
    Google Scholar
  • 22 McCray AT, Nelson SJ. The Representation of meaning in the UMLS. Meth Inform Med 1995; 34: 193-201.
    PubMedGoogle Scholar
  • 23 National Library of Medicine. Bethesda, Maryland: Medical Subject Headings. July 1986
    Google Scholar
  • 24 Bernauer J, Goldberg H. Compositional classification based on conceptual graphs. In: Andreassed S. et al., eds. Proc Artificial Intelligence in Medicine Europe. Amsterdam: IOS Press; 1993: 348-59.
    Google Scholar
  • 25 De Saussure F. Cours de Linguistique Géneralé. Paris: Payot; 1985
    Google Scholar
  • 26 Eco U. Le signe; Histoire et analyse d’un concept. Bruxelles: Editions Labor; 1988
    Google Scholar
  • 27 Bouaud J, Bachimont B, Charlet J, Zweigenbaum P, Boisvieux JF. Structuration and acquisition of an ontology within conceptual graphs. In: ICCS’94 Workshop of Knowledge Acquisition using Conceptllal Graph Theory. 1994
    PubMedGoogle Scholar