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DNA Computing Approach to Construction of Semantic Model

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3611)

Abstract

In this paper, after a new DNA-based semantic model is theoretically proposed, the preliminary experiment on construction of the small test model is successfully done. This model, referred to as ‘semantic model based on molecular computing’ (SMC) has the structure of a graph formed by the set of all (attribute, attribute values) pairs contained in the set of represented objects, plus a tag node for each object. Each path in the network, from an initial object-representing tag node to a terminal node represents the object named on the tag. Input of a set of input strands will result in the formation of object-representing dsDNAs via parallel self-assembly, from encoded ssDNAs representing both attributes and attribute values (nodes), as directed by ssDNA splinting strands representing relations (edges) in the network. The proposed model is rather suitable for knowledge representation in order to store vast amount of information with high density. The proposed model will appears as an interaction between AI and biomolecular computing research fields, and will be further extended for several AI applications.

Keywords

  • Knowledge Representation
  • Directed Edge
  • Terminal Node
  • Semantic Model
  • Pair Node

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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  • DOI: 10.1007/11539117_158
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© 2005 Springer-Verlag Berlin Heidelberg

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Tsuboi, Y., Ibrahim, Z., Kasai, N., Ono, O. (2005). DNA Computing Approach to Construction of Semantic Model. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_158

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  • DOI: https://doi.org/10.1007/11539117_158

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)