Conceptual Graph Grammar – A Simple Formalism for Sublanguage

S. B. Johnson

doi:10.1055/s-0038-1634565

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 1998; 37(04/05): 345-352
DOI: 10.1055/s-0038-1634565

Original Article

Schattauer GmbH

Conceptual Graph Grammar – A Simple Formalism for Sublanguage

S. B. Johnson

¹Department of Medical Informatics, Columbia University, New York, USA

› Institutsangaben

Abstract

There are a wide variety of computer applications that deal with various aspects of medical language: concept representation, controlled vocabulary, natural language processing, and information retrieval. While technical and theoretical methods appear to differ, all approaches investigate different aspects of the same phenomenon: medical sub language. This paper surveys the properties of medical sublanguage from a formal perspective, based on detailed analyses cited in the literature. A review of several computer systems based on sublanguage approaches shows some of the difficulties in addressing the interaction between the syntactic and semantic aspects of sUblanguage. A formalism called Conceptual Graph Grammar is presented that attempts to combine both syntax and semantics into a single notation by extending standard Conceptual Graph notation. Examples from the domain of pathology diagnoses are provided to illustrate the use of this formalism in medical language analysis. The strengths and weaknesses of the approach are then considered. Conceptual Graph Grammar is an attempt to synthesize the common properties of different approaches to sublanguage into a single formalism, and to begin to define a common foundation for language-related research in medical informatics.

Keywords

Medical Linguistics - Concept Representation - Controlled Vocabulary - Natural Language Processing

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Referenzen

REFERENCES
1 Friedman C, Johnson SB. Medical text processing: past achievements, future directions. In: Ball MJ, Collen MF. eds. Aspects of the Computer-Based Record. New York: Springer-Verlag; 1992
2 Johnson SB. Natural Language Processing in Biomedicine. In: Bronzino JD. The Handbook of Biomedical Engineering. Boca Raton (FL): CRC Press; 1995: 2768-73.
3 Grishman R, Kittredge R. eds. Analyzing Language in Restricted Domains: Sublanguage Description and Processing. Hillsdale, New Jersey: Erlbaum Associates; 1986
4 Kittredge R, Lehrberger J. eds. Sublanguage – Studies of language in restricted semantic domains. New York: De Gruyter; 1982
5 Sowa JF. Conceptual Graphs: information processing in mind and machine. Reading MA; Addison Wesley: 1984
6 Friedman C, Cimino JJ, Johnson SB. A conceptual model for clinical radiology reports. In: Safran C. ed Proceedings of the Seventeenth Annual Symposium on Computer Applications in Medical Care, 1993 Oct 3 to Nov 3; Washington (DC). New York: McGraw Hill; 1993: 829-33.
7 Campbell KE, Das AK, Musen MA. A Logical Foundation for Representation of Clinical Data. JAMIA 1994; 1 (3) 218-32.
8 Bell OS, Pattison-Gordon E, Greenes R. Experiments in Concept Modeling for Radiographic Image Reports. JAMIA 1994; 1 (3) 249-62.
9 Rassinoux A-M, Juge C, Michel P-A, Baud RH, Lemaitre O, Jean F-C, Degoulet P, Scherrer J-R. Analysis of Medical Jargon: The RECIT System. In: Barahona P, Stefanelli M, Wyatt J. eds Proceedings of Artificial Intelligence in Medicine (AIME 95). Berlin: Springer; 1995: 42-52.
10 Harris Z. Mathematical Structures of Language. New York: Wiley-Interscience; 1968
11 Sager N, Friedman C, Lyman M, Chi E, Macleod C, Chen J, Johnson SB. The analysis and processing of clinical narrative. Proceedings of the 5th Conference on Medical Informatics. 1986
12 Gabrieli E, Speth D. Automated Analysis of the Discharge Summary. Clinical Computing 1985; 15 (1) 1-28.
13 Sager N, Lyman M, Tick L, Nhan NT, Bucknail CEO. Natural language processing of asthma discharge summaries for the monitoring of patient care. SCAMC 1993; 265-8.
14 Hirschman L, Grishman R, Sager N. From Text to Structured Information: Automatic Processing of Medical Reports. AFIPS Conference Proceedings. Montvale, New Jersey: AFIPS Press; 1976. (45) 267-75.
15 Sager N. Natural Language Formatting: The Automatic Conversion of Texts to a Structured Database. In: Yovits M. ed Advances in Computers. New York: Academic Press; 1978. (17) 89-162.
16 Hirschman L, Sager N. Automatic information formatting of a medical sublanguage. In: Kittredge R, Lehrberger J. eds Sublanguage – Studies of language in restricted semantic domains. Berlin: De Gruyter; 1982: 27-80.
17 Johnson SB, Gottfried M. Sublanguage analysis as a basis for a controlled medical vocabulary. Proceedings of the Thirteenth Annual Symposium on Computer Applications in Medical Care. Washington, DC: November 1989: 519-23.
18 Sager N. SUblanguage: Linguistic Phenomenon, Computational Tool. In: Grishman R, Kittredge R. eds Analyzing Language in Restricted Domains: Sublanguage Description and Processing. Hillsdale, New Jersey: Erlbaum Associates; 1986
19 Harris Z. A Theory of Language and Information – A Mathematical Approach. Oxford: Clarendon Press; 1991
20 Starren J, Johnson SB. Notations for high effICIency data presentation in mammography. In: Cimino J. ed Proceedings of the AMIA Fall Symposium. 1996: 557-61.
21 Sager N, Friedman C, Lyman MS. Medical Language Processing: Computer Management of Narrative Data. Reading MA: Addison Wesley; 1987
22 Harris Z, Gottfried M, Ryckman T, Mattick P, Daladier A, Hams T, Harris S. The Form of Information in Science – Analysis of an Immunology Sublanguage. Boston, Mass: Kluwer Academic; 1989
23 Friedman C, Alderson PO, Austin HM, Cimino II, Johnson SB. A general natural language text processor for clinical radiology. JAMIA. 1994 (2).
24 Johnson SB, Aguirre A, Peng P, Cimino II. Interpreting natural language queries using the UMLS. In: Safran C. ed Proceedings of the Seventeenth Annual Symposium on Computer Applications in Medical Care 1993 Oct 3-Nov 3; Washington DC. New York: McGraw Hill; 1993: 294-8.
25 Schroder M. Knowledge-based Processing of Medical Language: A Language engineering approach. In: Advances in Artificial Intelligence, 16th German conference on AI. Berlin: Springer Verlag; 1992: 221-34.
26 Pereira FC, Warren DH. Definite clause grammars for language analysis – a survey of the formalism and a comparison with augmented transition networks. Artificial Intelligence 1980; (13) 231-78.
27 Harris Z. A Grammar of English on Mathematical Principles. New York: Wiley-Interscience; 1982
28 Hudson R. Word Grammar. London: Blackwell; 1984
29 Melcuk IA. Dependency Syntax: Theory and Practice. New York: State University Press of New York; 1988
30 Sowa JF. Toward the Expressive Power of Natural Language. In: Sowa JF. ed Principles of Semantic Networks: Explorations in the Representation of Knowledge. San Mateo, CA: Morgan Kaufmann Publishers; 1991: 157-89.
31 Velardi P, Pazienza MT, DeGiovanetti M. Conceptual Graphs for the Analysis and Generation of Sentences. IBM Journal of Research and Development 1986; 30 (1) 251-67.
32 McCray A. The UMLS Semantic Network. In: Proceedings of the Thirteenth Annual Symposium on Computer Applications in Medical Care. pp. 503-7 Washington, DC: IEEE Computer Society Press; 1989
33 McCray A, Sponsler J, Brylawski B, Browne A. The Role of Lexical Knowledge in Biomedical Text Understanding. In: Proceedings of the Eleventh Annual Symposium on Computer Applications in Medical Care. pp. 103-7 Washington, DC: IEEE Computer Society Press; 1987
34 National Library of Medicine. The SPECIALIST LEXICON. Bethesda, MD: Natural language systems group, National Library of Medicine; 1993
35 Harris Z, Mattick P. Science Sub languages and the Prospects for a Global Language of Science. Annals of the American Academy of Political and Social Science 1989; 495: 73-83.