Self-rganizing Hypernetworks

Abstract

Hypermedia is a style of building systems for information representation and management around a network of multi-media nodes connected together by typed links. Such systems have recently become quite popular due to their potential for aiding in the organization and manipulation of irregularly structured information. Many hypermedia systems, however, provide a set of predetermined nodes and links that may not be amended. This paper describes a new method to develop the organizational information structure from the view of the connectionist model. The method is based on the idea of self-organization and growth. In this paper, we describe a way of acquiring complex hierarchical structures among nodes in the networks. The growth starts from an initial interrelationship between nodes, and the network is let to organize the whole hierarchical structure. The growth process is guided through the self-organizing mechanism. The hierarchical representation model is designed for providing the global index for each node in the network that represents the hierarchical position of each node in the network. The hierarchical structured learning algorithm provides the mechanisms to create and modify the global indexes of the generated or existing nodes. With this learning algorithm, generated nodes are self-organized into the pre-existing networks. We develop the hypernetworks with self-organization and growth and show that the proposed model may represent a highly structured design for self-organizing hypermedia systems.