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Extracting and sharing knowledge from medical texts

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Abstract

In recent years, we have been developing a new framework for acquiring medical knowledge from Encyclopedic texts. This framework consists of three major parts. The first part is an extended high-level conceptual language (called HLCL 1.1) for use by knowledge engineers to formalize knowledge texts in an encyclopedia. The other part is an HLCL 1.1 compiler for parsing and analyzing the formalized texts into knowledge models. The third part is a set of domain-specific ontologies for sharing knowledge.

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Authors and Affiliations

  1. Laboratory of Intelligent Information Processing, Institute of Computing Technology, The Chinese Academy of Sciences, 100080, Beijing, P.R. China

    Cao Cungen

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  1. Cao Cungen
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Correspondence to Cao Cungen.

Additional information

This work is supported by a grant from the Chinese Academy of Science (No.#2000-4010), and a grant from National Natural Science Foundation of China (No.#20010010-A), a grant from the Ministry of Science and Technology (#0221CCA03000).

CAO Cungen is a professor of artificial intelligence. His main interests include knowledge acquisition and knowledge-based systems.

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Cao, C. Extracting and sharing knowledge from medical texts. J. of Comput. Sci. & Technol. 17, 295–303 (2002). https://doi.org/10.1007/BF02947307

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  • DOI: https://doi.org/10.1007/BF02947307

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