Abstract
Knowledge discovery from knowledge bases is an important problem in the field of data mining because it can solve the problem of a lack of knowledge, which is a bottleneck in intelligent systems based on knowledge bases. With the expansion of knowledge bases and structured data becoming more complex, first-order logic is no longer capable of knowledge representation and induction, so higher-order logic is naturally used in this case. In this paper, the process by which knowledge representation is adopted from first-order logic to higher-order logic is discussed, and a decision-tree algorithm learned with higher-order logic is also presented. Experimental results show that the proposed algorithm is efficient.
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Acknowledgements
In this paper, the research was sponsored by open fund of Guangxi Key laboratory of hybrid computation and IC design analysis and the fund of the Department of Education of Guangxi Province in China.
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Li, G. (2015). Knowledge Discovery from Knowledge Bases with Higher-Order Logic. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_53
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DOI: https://doi.org/10.1007/978-3-319-11104-9_53
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11103-2
Online ISBN: 978-3-319-11104-9
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