International Journal of Hybrid Intelligent Systems 2 (2005) 1–12 1 IOS Press DNA computing approach to semantic knowledge representation Yusei Tsuboi ∗ , Zuwairie Ibrahim and Osamu Ono Institute of Applied DNA Computing, Graduate School of Science & Technology, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa, 214-8571, Japan Abstract. DNA computing has a lot of potential, in terms of ability to implement a relational database with circular molecules. In this work, a new DNA-based semantic model is proposed and described theoretically for implementing DNA based memories. 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-value 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 (value, attribute)-pairs (nodes), as directed by ssDNA splinting strands representing relations (edges) in the network. The computational complexity of the implementation is estimated via simple simulation, which indicates the advantage of the approach over a sequential model. We believe that the semantic models are rather suitable for DNA-based memory, and that this proposal is the first such approach in the semantic networks area. 1. Introduction Our research group focuses on development in the area of semantic networks (semantic nets) [14], via a new computational paradigm. Human information processing often involves comparing concepts. There are various ways of assessing concept similarity, which vary depending on the adopted model of knowledge representation. In featural representations, concepts are represented by sets of features. In Quillian’s model of semantic memory, concepts are represented by the relationship name via links. Links are labeled by the name of the relationship and are assigned criteriality tags that attest to the importance of the link. In artificial computer implementations, criteriality tags are numerical values the represent the degree of association between concept pairs (i.e., how often the link is traversed), and the nature of the association. The association is positive if the existence of that link indicates some sort of similarity between the end nodes, and negative otherwise. For example, superordinate links (the term used for ‘is-a ...’ relationships) have a positive association, while “is-not-a” links have a negative association. Just as there are at least two research communities that deal necessarily with questions of generalization in science, there are at least two bodies of knowledge concerned with representation of the known world as discovered and explained by science. On one hand, knowledge can be fundamentally procedural and causal; on the other, knowledge is fundamentally judgemental [19]. Naturally, the knowledge representation schemas are quite different; thus, the manners in which knowledge may be processed to generate new knowledge in the two models are also quite different. Semantic modeling provides a richer * Corresponding author. Tel.: +81 44 934 7289; Fax: +81 44 934 7909; E-mail: tsuboi@isc.meiji.ac.jp. 1448-5869/05/$17.00  2005 – IOS Press, AKI and the authors. All rights reserved