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Combining representation formalisms for reasoning upon mathematical knowledge

Published: 05 December 2023 Publication History

Abstract

Knowledge in mathematics (definitions, theorems, proofs, etc.) is usually expressed in a way that combines natural language and mathematical expressions (e.g. equations). Using an ontology formalism such as OWL DL is well-suited for formalizing the natural language part, but complex mathematical expressions can be better handled by symbolic computation systems. We examine this representation issue and propose an original extension of OWL DL by call formulas, i.e., formulas from which assertions can be drawn thanks to calls to external functions. Using this formalism makes it possible to classify a mathematical problem defined by its relations to instances and classes and by some mathematical expressions: if a theorem for solving this problem is represented in the knowledge base, it can be retrieved, and thus, the problem can be solved by applying this theorem. We describe an inference algorithm and discuss its properties as well as its limitations. Indeed, the proposed extension, algorithm, and implementation represent a first step towards a combined formalism for representing mathematical knowledge, with some open issues regarding the representation of more complex problems: the resolution of multiscale, multiphysics cases in physics are foreseen.

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W. Belkhir, N. Ratier, D. D. Nguyen, B. Yang, M. Lenczner, F. Zamkotsian, and H. Cirstea. 2015. Towards an automatic tool for multi-scale model derivation illustrated with a micro-mirror array. In 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 47–54.
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        cover image ACM Conferences
        K-CAP '23: Proceedings of the 12th Knowledge Capture Conference 2023
        December 2023
        270 pages
        ISBN:9798400701412
        DOI:10.1145/3587259
        • Editors:
        • Brent Venable,
        • Daniel Garijo,
        • Brian Jalaian
        Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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        New York, NY, United States

        Publication History

        Published: 05 December 2023

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        Author Tags

        1. knowledge modeling
        2. knowledge representation
        3. mathematical knowledge

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        • CNRS

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        K-CAP '23
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        K-CAP '23: Knowledge Capture Conference 2023
        December 5 - 7, 2023
        FL, Pensacola, USA

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