The Knowledge Grid

The following sections are included:

• Foreword

• Preface

• Contents

The development of science and technology has extended human behavior and sensation, accelerated the progress of society, and enabled people to understand the physical space and themselves more profoundly. But, we still have much to find out, especially about knowledge.

Humans live with networks of versatile relations. What are the essential characteristics of relational networking? What are the laws, methods and models for relational networking? How do the relational networks emerge semantics? How to make use of the relational networks to support basic intelligence? One of the major tasks of science is to unveil various relations.

Classification is one of the most basic methods for humans to recognize and organize various resources. The Resource Space Model is a resource organization model based on multi-dimensional classifications.

Intelligent individuals have the ability of classifying feelings through multiple channels, generating a single semantic image by linking relevant semantic images and abstracting semantic images to classes, retrieving the required semantic image on demand, and acting based on the emerging semantic images.

A major goal of the underlying resource management mechanism of the Knowledge Grid environment is to efficiently obtain, store and manage various resources (may be in different spaces) on demand through semantic images. The single semantic image is the fusion of semantic images of different facets on one resource or a set of resources.

Knowledge is power, but knowledge is not just statically stored. It evolves through being shared and developed by various roles, people, and resources within the cyber-physical-socio environment.

A knowledge flow is a passing of knowledge between people or through machines. It has three crucial attributes: direction (sender and receiver), carrier (medium) and content (shareable). Good knowledge flow enables intelligent participants (people, roles and devices) to cooperate effectively.

Scale-free networks are often dominated overall by relatively few nodes that are linked to relatively many other nodes. This chapter describes the properties of diverse scale-free networks and reviews the development of methods for modeling such networks, covering random graph theory, small-world effects, the idea of preferential attachment, and the dynamic evolution of these networks. By extending the current scale-free network model, we explore an abstract live network—a possible high-level model for Knowledge Grid development.

The rich get richer phenomenon widely exists in social networks. There are two ways for a node in social network to become richer: linking to more nodes; and, linking to more important nodes. A node may get rise if it links to some important nodes than links to many but less important nodes. Both nodes and links play an important role in forming network centrality. Topological centrality is a network centrality measure that reflects relative centrality of nodes and links according to the influence between nodes, between links, and between node and link.

Recommending, querying and answering usually involve in one knowledge sharing process. A self-evolving and adaptive peer-to-peer semantic link network can support efficient decentralized knowledge sharing.

Building semantic overlays over P2P networks is a way to realize efficient information and knowledge services over large-scale networks. The problems are: how a P2P network can be adaptive for high-level semantic information management, and how semantic information can be efficiently managed over the network. The first problem concerns network topology and routing. The second problem concerns publishing, indexing and querying complex semantic information.

This chapter suggests a set of scalable solutions for inspiring better innovations. The structured approaches mainly use the structured topology as the underlying network infrastructure to support exact and efficient complex query. Unstructured approaches build the network topology in a loosely coupled way without too much rules and restrictions and queries are processed through random walk or broadcasting methods.

Energy is the most basic force that drives the motion of various individuals (including machines and organisms) and the execution and evolution of various spaces. It is becoming more and more important to save and efficiently use energy for ensuring harmonious development of the complex space consisting of cyber space, physical space and social space.

The following sections are included:

• Bibliography

• Index