Machine-negotiated, ontology-based EDI (Electronic Data Interchange)

  • Fritz Lėhmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1028)


  1. 1.

    EDI is held back by inflexibility and complexity for users. This is partly due to the antiquated X12 and EDIFACT standards.

  2. 2.

    Truly automatic translation between two disparate databases, or between EDI and a database not set up just for EDI, requires machine representation of the concepts and meaning of the data schema, not just the schema.

  3. 3.

    The database world has reluctantly begun to recognize this, and that common ontologies are needed for true automated integration. The EDI community, due to its particular culture, may never realize it.

  4. 4.

    The real-world “common ontology” is actually the most valuable thing about EDIFACT and X12, but it is informal, in English, and unavailable for any computational use.

  5. 5.

    The current major Artificial Intelligence efforts to build large generic ontologies should be applied to automate EDI translations and do useful inference; also, AI ontologists can exploit thousands of practical real-world concept-categories from EDI standards EDIFACT and X12. These provide good target concepts for us to define.



Business Form Enterprise Model Electronic Data Interchange Conceptual Graph Purchase Order 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Fritz Lėhmann
    • 1
    • 2
  1. 1.GRANDAI SoftwareIrvine
  2. 2.Center for Optimization and Semantic ControlWashington UniversitySaint LouisUSA

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