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2010 Volume 25
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RESEARCH ARTICLE   Open Access    

A survey of formalisms for representing and reasoning with scientific knowledge

  • 1.

    Department of Computer Science

  • 2.

    School of Electronics

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RESEARCH ARTICLE   Open Access    

A survey of formalisms for representing and reasoning with scientific knowledge

  • 1.

    Department of Computer Science

  • 2.

    School of Electronics

  • Published online: 01 June 2010
The Knowledge Engineering Review  25,  Article number: 10.1017/S0269888910000019  (2010)  |  Cite this article

Abstract: Abstract: With the rapid growth in the quantity and complexity of scientific knowledge available for scientists, and allied professionals, the problems associated with harnessing this knowledge are well recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by harnessing background knowledge, the querying and combining tasks can be carried out more intelligently. In this paper, we review some of the significant proposals for formalisms for representing and reasoning with scientific knowledge.

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    • From N Yildirim, A Sahin, and S Gultekin (2008) The effect of latanoprost, bimatoprost, and travoprost on circadian variation of intraocular pressure in patients with open-angle glaucoma, J. Glaucoma 17(1): 36–9.

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    Anthony Hunter, Weiru Liu. 2010. A survey of formalisms for representing and reasoning with scientific knowledge. The Knowledge Engineering Review. 25: doi: 10.1017/S0269888910000019
    Anthony Hunter, Weiru Liu. 2010. A survey of formalisms for representing and reasoning with scientific knowledge. The Knowledge Engineering Review. 25: doi: 10.1017/S0269888910000019
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